{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T14:42:06Z","timestamp":1775745726799,"version":"3.50.1"},"reference-count":185,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T00:00:00Z","timestamp":1690502400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T00:00:00Z","timestamp":1690502400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-15576-7","type":"journal-article","created":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T08:01:57Z","timestamp":1690531317000},"page":"19683-19728","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":72,"title":["Medical images classification using deep learning: a survey"],"prefix":"10.1007","volume":"83","author":[{"given":"Rakesh","family":"Kumar","sequence":"first","affiliation":[]},{"given":"Pooja","family":"Kumbharkar","sequence":"additional","affiliation":[]},{"given":"Sandeep","family":"Vanam","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9598-242X","authenticated-orcid":false,"given":"Sanjeev","family":"Sharma","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,28]]},"reference":[{"key":"15576_CR1","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1016\/j.neunet.2020.03.017","volume":"126","author":"MS Abadeh","year":"2020","unstructured":"Abadeh MS, Shahamat H (2020) Brain MRI analysis using a deep learning based evolutionary approach. Neural Netw 126:218\u2013234. https:\/\/doi.org\/10.1016\/j.neunet.2020.03.017","journal-title":"Neural Netw"},{"key":"15576_CR2","doi-asserted-by":"publisher","unstructured":"Abdulkareem K et al (2022) Automated system for identifying COVID-19 Infections in computed tomography images using deep learning models. In: Journal of healthcare engineering 2022. https:\/\/doi.org\/10.1155\/2022\/5329014","DOI":"10.1155\/2022\/5329014"},{"key":"15576_CR3","doi-asserted-by":"publisher","first-page":"3511","DOI":"10.1109\/ACCESS.2023.3233969","volume":"11","author":"SM Abdullah","year":"2023","unstructured":"Abdullah SM et al (2023) Deep transfer learning based parkinson\u2019s disease detection using optimized feature selection. IEEE Access 11:3511\u20133524. https:\/\/doi.org\/10.1109\/ACCESS.2023.3233969","journal-title":"IEEE Access"},{"key":"15576_CR4","doi-asserted-by":"publisher","first-page":"1295","DOI":"10.3390\/electronics11091295","volume":"11","author":"A Abdulsahib","year":"2022","unstructured":"Abdulsahib A, Mahmoud M (2022) An Automated Image Segmentation and Useful Feature Extraction Algorithm for Retinal Blood Vessels in Fundus Images. Electronics 11:1295. https:\/\/doi.org\/10.3390\/electronics11091295","journal-title":"Electronics"},{"key":"15576_CR5","doi-asserted-by":"crossref","first-page":"22812","DOI":"10.1109\/ACCESS.2020.2970023","volume":"8","author":"ZU Abideen","year":"2020","unstructured":"Abideen ZU, Ghafoor M, Munir K, Saqib M, Ullah A, Zia T, Tariq SA, Ahmed G, Zahra A (2020) Uncertainty assisted robust tuberculosis identification with bayesian convolutional neural networks. IEEE Access 8:22812\u201322825. IEEE","journal-title":"IEEE Access"},{"key":"15576_CR6","doi-asserted-by":"publisher","first-page":"52575","DOI":"10.1109\/ACCESS.2020.2980728","volume":"8","author":"ZA Al-Saffar","year":"2020","unstructured":"Al-Saffar ZA, Yildirim T (2020) A novel approach to improving brain image classification using mutual information-accelerated singular value decomposition. IEEE Access 8:52575\u201352587. https:\/\/doi.org\/10.1109\/ACCESS.2020.2980728","journal-title":"IEEE Access"},{"key":"15576_CR7","doi-asserted-by":"publisher","first-page":"64279","DOI":"10.1109\/ACCESS.2019.2916849","volume":"7","author":"I Allaouzi","year":"2019","unstructured":"Allaouzi I, Ahmed BM (2019) A novel approach for multi-label chest x-ray classification of common thorax diseases. IEEE Access 7:64279\u201364288. https:\/\/doi.org\/10.1109\/ACCESS.2019.2916849","journal-title":"IEEE Access"},{"key":"15576_CR8","doi-asserted-by":"publisher","unstructured":"Alzubaidi LZ, Humaidi J (2021) Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 8.53. https:\/\/doi.org\/10.1186\/s40537-021-00444-8","DOI":"10.1186\/s40537-021-00444-8"},{"key":"15576_CR9","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1049\/iet-cvi.2014.0193","volume":"10","author":"V Anitha","year":"2016","unstructured":"Anitha V, Murugavalli S (2016) Brain tumour classification using two-tier classifier with adaptive segmentation technique. IET Comput Vis 10:9\u201317. https:\/\/doi.org\/10.1049\/iet-cvi.2014.0193","journal-title":"IET Comput Vis"},{"key":"15576_CR10","doi-asserted-by":"crossref","unstructured":"Ansingkar NP, Patil R, Deshmukh PD (2022) An efficient multi class Alzheimer detection using hybrid equilibrium optimizer with capsule auto encoder. Multimedia Tools and Applications, pp 1\u201332","DOI":"10.1007\/s11042-021-11786-z"},{"key":"15576_CR11","doi-asserted-by":"publisher","first-page":"100138","DOI":"10.1016\/j.mlwa.2021.100138","volume":"6","author":"D Arias-Garz\u00f3n","year":"2021","unstructured":"Arias-Garz\u00f3n D et al (2021) COVID-19 detection in X-ray images using convolutional neural networks. Mach Learn Appl 6:100138. ISSN: 2666-8270. https:\/\/doi.org\/10.1016\/j.mlwa.2021.100138, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2666827021000694","journal-title":"Mach Learn Appl"},{"key":"15576_CR12","doi-asserted-by":"publisher","first-page":"105659","DOI":"10.1109\/ACCESS.2020.2998808","volume":"8","author":"R Ashraf","year":"2020","unstructured":"Ashraf R et al (2020) Deep convolution neural network for big data medical image classification. IEEE Access 8:105659\u2013105670. https:\/\/doi.org\/10.1109\/ACCESS.2020.2998808","journal-title":"IEEE Access"},{"key":"15576_CR13","doi-asserted-by":"publisher","first-page":"5455","DOI":"10.1007\/s10462-020-09825-6","volume":"53","author":"K Asifullah","year":"2020","unstructured":"Asifullah K et al (2020) A survey of the recent architectures of deep convolutional neural networks. Artif Intell Rev 53:5455\u20135516. ISSN: 1573-7462. https:\/\/doi.org\/10.1007\/s10462-020-09825-6","journal-title":"Artif Intell Rev"},{"key":"15576_CR14","unstructured":"Baldi P (2011) Autoencoders, unsupervised learning and deep architectures. In: Proceedings of the 2011 International Conference on Unsupervised and Transfer Learning Workshop - vol 27. UTLW\u201911. Washington, USA: JMLR.org, pp 37\u201350"},{"key":"15576_CR15","unstructured":"Bank D, Koenigstein N, Giryes R (2021) Autoencoders. arXiv:2003.05991[cs.LG.]"},{"key":"15576_CR16","doi-asserted-by":"publisher","first-page":"66052","DOI":"10.1109\/ACCESS.2021.3076533","volume":"9","author":"J Bian","year":"2021","unstructured":"Bian J et al (2021) Skin lesion classification by multi-view filtered transfer learning. IEEE Access 9:66052\u201366061. https:\/\/doi.org\/10.1109\/ACCESS.2021.3076533","journal-title":"IEEE Access"},{"key":"15576_CR17","unstructured":"Blood Cell Images (2018) https:\/\/www.kaggle.com\/paultimothymooney\/blood-cells"},{"key":"15576_CR18","unstructured":"Brain MRI Images for Brain Tumor Detection (2019) https:\/\/www.kaggle.com\/navoneel\/brain-mri-images-for-brain-tumor-detection"},{"key":"15576_CR19","unstructured":"Brain-Tumor-Progression (2021) https:\/\/wiki.cancerimagingarchive.net\/display\/Public\/Brain-Tumor-Progression#g339481190e2ccc0d07d7455ab87b3ebb625adf48"},{"key":"15576_CR20","doi-asserted-by":"crossref","unstructured":"Brima Y, Tushar MHK, Kabir U, Islam T (2021) Deep transfer learning for brain magnetic resonance image multi-class classification. arXiv:2106.07333[cs.CV]","DOI":"10.3329\/dujase.v6i2.59215"},{"key":"15576_CR21","unstructured":"COPD Machine Learning Datasets (2018) http:\/\/bigr.nl\/research\/projects\/copd"},{"key":"15576_CR22","unstructured":"COVID-19 Radiography Dataset (2020) https:\/\/www.kaggle.com\/tawsifurrahman\/covid19-radiography-database"},{"key":"15576_CR23","unstructured":"CT Images in COVID-19 (2021) https:\/\/wiki.cancerimagingarchive.net\/display\/Public\/CT+Images+in+COVID-19"},{"key":"15576_CR24","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.knosys.2018.07.043","volume":"161","author":"Y Chai","year":"2018","unstructured":"Chai Y, Liu H, Xu J (2018) Glaucoma diagnosis based on both hidden features and domain knowledge through deep learning models. Knowl Based Syst 161:147\u2013156. ISSN:0950-7051. https:\/\/doi.org\/10.1016\/j.knosys.2018.07.043, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0950705118303940","journal-title":"Knowl Based Syst"},{"key":"15576_CR25","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.inffus.2017.12.007","volume":"44","author":"D Charte","year":"2018","unstructured":"Charte D, Charte F, Garca S, del Jesus MJ, Herrera F (2018) A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines. Inf Fusion 44:78\u201396. ISSN: 1566-2535. https:\/\/doi.org\/10.1016\/j.inffus.2017.12.007, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1566253517307844.","journal-title":"Inf Fusion"},{"key":"15576_CR26","unstructured":"Chest X-Ray Images (Pneumonia) (2018) https:\/\/www.kaggle.com\/paultimothymooney\/chest-xray-pneumonia."},{"key":"15576_CR27","doi-asserted-by":"crossref","unstructured":"Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. arXiv:1610.02357[cs.CV]","DOI":"10.1109\/CVPR.2017.195"},{"key":"15576_CR28","doi-asserted-by":"publisher","first-page":"38","DOI":"10.4018\/IJHISI.2016040103","volume":"11.2","author":"CL Chowdhary","year":"2016","unstructured":"Chowdhary CL, Acharjya D (2016) A hybrid scheme for breast cancer detection using intuitionistic fuzzy rough set technique. Int J Healthc Inf Syst Inform 11.2:38\u201361. https:\/\/doi.org\/10.4018\/IJHISI.2016040103","journal-title":"Int J Healthc Inf Syst Inform"},{"key":"15576_CR29","doi-asserted-by":"publisher","unstructured":"Chowdhary CL, Acharjya D (2016) Breast cancer detection using intuitionistic fuzzy histogram hyperbolization and possibilitic fuzzy c-mean clustering algorithms with texture feature based classification on mammography images. In: Proceedings of the international conference on advances in information communication technology & computing. https:\/\/doi.org\/10.1145\/2979779.2979800","DOI":"10.1145\/2979779.2979800"},{"key":"15576_CR30","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.procs.2020.03.179","volume":"167","author":"CL Chowdhary","year":"2020","unstructured":"Chowdhary CL, Acharjya D (2020) Segmentation and feature extraction in medical imaging: A systematic review. Procedia Comput Sci 167:26\u201336. https:\/\/doi.org\/10.1016\/j.procs.2020.03.179","journal-title":"Procedia Comput Sci"},{"key":"15576_CR31","doi-asserted-by":"publisher","unstructured":"DA Zebari, DA Ibrahim, HJ Mohammed (2022) Effective hybrid deep learning model for COVID-19 patterns identification using CT images. Expert Systems. https:\/\/doi.org\/10.1111\/exsy.13010","DOI":"10.1111\/exsy.13010"},{"key":"15576_CR32","unstructured":"DRIVE: Digital Retinal Images for Vessel Extraction (2012) https:\/\/drive.grand-challenge.org\/"},{"issue":"1","key":"15576_CR33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LSENS.2019.2963712","volume":"4","author":"V Das","year":"2020","unstructured":"Das V, Dandapat S, Bora PK (2020) A data-efficient approach for automated classification of oct images using generative adversarial network. IEEE Sens Lett 4(1):1\u20134. IEEE","journal-title":"IEEE Sens Lett"},{"key":"15576_CR34","doi-asserted-by":"publisher","unstructured":"Das PK, Meher S (2021) An efficient deep convolutional neural network based detection and classification of acute lymphoblastic leukemia. Expert Systems with Applications, pp 115311. ISSN: 0957-4174. https:\/\/doi.org\/10.1016\/j.eswa.2021.115311, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0957417421007405","DOI":"10.1016\/j.eswa.2021.115311"},{"key":"15576_CR35","doi-asserted-by":"publisher","first-page":"213502","DOI":"10.1109\/ACCESS.2020.3040106","volume":"8","author":"K Das","year":"2020","unstructured":"Das K et al (2020) Detection of breast cancer from whole slide histopathological images using deep multiple instance CNN. IEEE Access 8:213502\u2013213511. https:\/\/doi.org\/10.1109\/ACCESS.2020.3040106","journal-title":"IEEE Access"},{"key":"15576_CR36","doi-asserted-by":"publisher","first-page":"110713","DOI":"10.1016\/j.chaos.2021.110713","volume":"144","author":"AK Das","year":"2021","unstructured":"Das AK et al (2021) TLCoV- An automated Covid-19 screening model using Transfer Learning from chest X-ray images. Chaos, Solitons Fractals 144:110713. ISSN: 0960\u20130779. https:\/\/doi.org\/10.1016\/j.chaos.2021.110713, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0960077921000667","journal-title":"Chaos, Solitons Fractals"},{"key":"15576_CR37","doi-asserted-by":"publisher","first-page":"195594","DOI":"10.1109\/ACCESS.2020.3033762","volume":"8","author":"J De Moura","year":"2020","unstructured":"De Moura J et al (2020) Deep convolutional approaches for the analysis of COVID-19 using chest X-Ray images from portable devices. IEEE Access 8:195594\u2013195607. https:\/\/doi.org\/10.1109\/ACCESS.2020.3033762","journal-title":"IEEE Access"},{"key":"15576_CR38","doi-asserted-by":"publisher","first-page":"107160","DOI":"10.1016\/j.asoc.2021.107160","volume":"103","author":"F Demir","year":"2021","unstructured":"Demir F (2021) DeepCoroNet: A deep LSTM approach for automated detection of COVID-19 cases from chest X-ray images. IEEE Access 103:107160. https:\/\/doi.org\/10.1016\/j.asoc.2021.107160","journal-title":"IEEE Access"},{"key":"15576_CR39","unstructured":"Diabetic Retinopathy Detection (2015) https:\/\/www.kaggle.com\/c\/diabeticretinopathy-detection"},{"key":"15576_CR40","doi-asserted-by":"publisher","first-page":"1083","DOI":"10.1049\/ipr2.12087","volume":"15","author":"J Diakite","year":"2021","unstructured":"Diakite J, Xiaping X (2021) Hyperspectral image classification using 3D 2D CNN. IET Image Proc 15:1083\u20131092. https:\/\/doi.org\/10.1049\/ipr2.12087","journal-title":"IET Image Proc"},{"key":"15576_CR41","doi-asserted-by":"publisher","first-page":"166405","DOI":"10.1016\/j.ijleo.2021.166405","volume":"231","author":"AS Elkorany","year":"2021","unstructured":"Elkorany AS, Elsharkawy ZF (2021) COVIDetection-Net: A tailored COVID-19 detection from chest radiography images using deep learning. Optik 231:166405. ISSN: 0030-4026. https:\/\/doi.org\/10.1016\/j.ijleo.2021.166405, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0030402621001388","journal-title":"Optik"},{"issue":"1","key":"15576_CR42","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.2991\/ijcis.d.210301.002","volume":"14","author":"H Elmannai","year":"2021","unstructured":"Elmannai H, Hamdi M, AlGarni A (2021) Deep learning models combining for breast cancer histopathology image classification. Int J Comput Intell Syst 14(1):1003. Atlantis Press BV","journal-title":"Int J Comput Intell Syst"},{"key":"15576_CR43","doi-asserted-by":"crossref","first-page":"41711","DOI":"10.1007\/s11042-021-11268-2","volume":"81.29","author":"M Fradi","year":"2022","unstructured":"Fradi M, Khriji L, Machhout M (2022) Real-time arrhythmia heart disease detection system using CNN architecture based various optimizers-networks. Multimed Tools Appl 81.29:41711\u201341732","journal-title":"Multimed Tools Appl"},{"key":"15576_CR44","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.neucom.2018.09.013","volume":"321","author":"M Frid-Adar","year":"2018","unstructured":"Frid-Adar M et al (2018) GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing 321:321\u2013331. ISSN: 0925-2312. https:\/\/doi.org\/10.1016\/j.neucom.2018.09.013, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0925231218310749","journal-title":"Neurocomputing"},{"key":"15576_CR45","doi-asserted-by":"publisher","unstructured":"Gao Y, Wang R et al, Shi Y (2013) Transductive cost-sensitive lung cancer image classification. Appl Intell springer 38:16\u201328. https:\/\/doi.org\/10.1007\/s10489-012-0354-z","DOI":"10.1007\/s10489-012-0354-z"},{"key":"15576_CR46","doi-asserted-by":"publisher","unstructured":"Garc\u00eda-Ord\u00e1s MT et al (2020) Detecting respiratory pathologies using convolutional neural networks and variational autoencoders for unbalancing data. Sensors 20.4. ISSN: 1424-8220. https:\/\/doi.org\/10.3390\/s20041214, https:\/\/www.mdpi.com\/1424-8220\/20\/4\/1214","DOI":"10.3390\/s20041214"},{"key":"15576_CR47","first-page":"2725","volume":"32","author":"NK Garg","year":"2018","unstructured":"Garg NK, Chhabra P, Kumar M (2018) Content-based image retrieval system using ORB and SIFT features. Neural Comput Appl 32:2725\u20132733","journal-title":"Neural Comput Appl"},{"key":"15576_CR48","unstructured":"Goodfellow IJ et al (2014) Generative adversarial networks. arXiv:1406.2661[stat.ML]"},{"key":"15576_CR49","doi-asserted-by":"publisher","first-page":"5929","DOI":"10.1007\/s10462-020-09838-1","volume":"53","author":"VH Greg","year":"2020","unstructured":"Greg VH, Carlos M, Gonzalo N (2020) A review on the long short-term memory model. Artif Intell Rev 53:5929\u20135955. ISSN: 1573\u20137462. https:\/\/doi.org\/10.1007\/s10462-020-09838-1","journal-title":"Artif Intell Rev"},{"key":"15576_CR50","doi-asserted-by":"crossref","unstructured":"Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Liu T, Wang X, Wang L, Wang G, Cai J, Chen T (2017) Recent advances in convolutional neural networks. arXiv:1512.07108[cs.CV]","DOI":"10.1016\/j.patcog.2017.10.013"},{"key":"15576_CR51","doi-asserted-by":"crossref","first-page":"527","DOI":"10.3390\/s23010527","volume":"23.1","author":"MM Hasan","year":"2023","unstructured":"Hasan MM et al (2023) Review on the evaluation and development of artificial intelligence for COVID-19 containment. Sensors 23.1:527","journal-title":"Sensors"},{"key":"15576_CR52","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.neucom.2020.04.044","volume":"405","author":"X He","year":"2020","unstructured":"He X, Fang L, Rabbani H, Chen X, Liu Z (2020) Retinal optical coherence tomography image classification with label smoothing generative adversarial network. Neurocomputing 405:37\u201347. ISSN: 0925-2312. https:\/\/doi.org\/10.1016\/j.neucom.2020.04.044, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0925231220306111","journal-title":"Neurocomputing"},{"key":"15576_CR53","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. arXiv:1512.03385[ cs.CV]","DOI":"10.1109\/CVPR.2016.90"},{"key":"15576_CR54","unstructured":"Heart Dieses Data Set (1988) http:\/\/archive.ics.uci.edu\/ml\/datasets\/Heart+Disease"},{"key":"15576_CR55","doi-asserted-by":"publisher","first-page":"4275","DOI":"10.1109\/ACCESS.2018.2885639","volume":"7","author":"DJ Hemanth","year":"2019","unstructured":"Hemanth DJ et al (2019) A modified deep convolutional neural network for abnormal brain image classification. IEEE Access 7:4275\u20134283. https:\/\/doi.org\/10.1109\/ACCESS.2018.2885639","journal-title":"IEEE Access"},{"key":"15576_CR56","unstructured":"Histology Image Collection Library (1988) https:\/\/medisp.bme.uniwa.gr\/hicl\/index.html"},{"key":"15576_CR57","unstructured":"Howard AG et al (2017) MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861[cs.CV]"},{"issue":"2","key":"15576_CR58","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1109\/TCYB.2017.2778799","volume":"49","author":"F Liao","year":"2017","unstructured":"Liao F, Chen X, Hu X, Song S (2017) Estimation of the volume of the left ventricle from MRI images using deep neural networks. IEEE Trans Cybern 49(2):495\u2013504. IEEE","journal-title":"IEEE Trans Cybern"},{"key":"15576_CR59","doi-asserted-by":"crossref","unstructured":"Hu J et al (2019) Squeeze-and-excitation networks. arXiv:709.01507[cs.CV]","DOI":"10.1109\/CVPR.2018.00745"},{"key":"15576_CR60","doi-asserted-by":"publisher","first-page":"118869","DOI":"10.1109\/ACCESS.2020.3005510","volume":"8","author":"S Hu","year":"2020","unstructured":"Hu S et al (2020) Weakly supervised deep learning for COVID-19 infection detection and classification from CT images. IEEE Access 8:118869\u2013118883. https:\/\/doi.org\/10.1109\/ACCESS.2020.3005510","journal-title":"IEEE Access"},{"key":"15576_CR61","doi-asserted-by":"crossref","first-page":"33179","DOI":"10.1007\/s11042-021-11403-z","volume":"80","author":"Z-P Hu","year":"2021","unstructured":"Hu Z-P, Zhang R-X, Qiu Y, Zhao M-Y, Sun Z (2021) 3D convolutional networks with multi-layer-pooling selection fusion for video classification. Multimed Tools Appl 80:33179\u201333192. Springer","journal-title":"Multimed Tools Appl"},{"key":"15576_CR62","doi-asserted-by":"crossref","unstructured":"Huang G et al (2018) Densely connected convolutional networks. arXiv:1608.06993[cs.CV]","DOI":"10.1109\/BigMM.2018.8499078"},{"key":"15576_CR63","doi-asserted-by":"publisher","first-page":"1026","DOI":"10.1109\/LSP.2019.2917779","volume":"26.7","author":"L Huang","year":"2019","unstructured":"Huang L, Fang L, Rabbani H, Chen X (2019) Automatic classification of retinal optical coherence tomography images with layer guided convolutional neural network. IEEE Signal Process Lett 26.7:1026\u20131030. https:\/\/doi.org\/10.1109\/LSP.2019.2917779","journal-title":"IEEE Signal Process Lett"},{"key":"15576_CR64","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1109\/JBHI.2019.2905623","volume":"24.1","author":"Q Huang","year":"2020","unstructured":"Huang Q et al (2020) Blood cell classification based on hyperspectral imaging with modulated Gabor and CNN. IEEE J Biomed Health Inf 24.1:160\u2013170. https:\/\/doi.org\/10.1109\/JBHI.2019.2905623","journal-title":"IEEE J Biomed Health Inf"},{"key":"15576_CR65","doi-asserted-by":"publisher","first-page":"106230","DOI":"10.1016\/j.knosys.2020.106230","volume":"204","author":"X Huang","year":"2020","unstructured":"Huang X et al (2020) Deep transfer convolutional neural network and extreme learning machine for lung nodule diagnosis on CT images. Knowl-Based Syst 204:106230. ISSN: 0950-7051. https:\/\/doi.org\/10.1016\/j.knosys.2020.106230, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0950705120304378","journal-title":"Knowl-Based Syst"},{"key":"15576_CR66","doi-asserted-by":"publisher","first-page":"101347","DOI":"10.1016\/j.tice.2020.101347","volume":"65","author":"E Hussain","year":"2020","unstructured":"Hussain E et al (2020) A comprehensive study on the multi-class cervical cancer diagnostic prediction on pap smear images using a fusion-based decision from ensemble deep convolutional neural network. Tissue Cell 65:101347. ISSN: 0040-8166. https:\/\/doi.org\/10.1016\/j.tice.2020.101347, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0040816619304872","journal-title":"Tissue Cell"},{"key":"15576_CR67","doi-asserted-by":"publisher","first-page":"122627","DOI":"10.1109\/ACCESS.2022.3223704","volume":"10","author":"SM Hussain","year":"2022","unstructured":"Hussain SM et al (2022) Deep learning based image processing for robot assisted surgery: a systematic literature survey. IEEE Access 10:122627\u2013122657. https:\/\/doi.org\/10.1109\/ACCESS.2022.3223704","journal-title":"IEEE Access"},{"key":"15576_CR68","unstructured":"Indian Diabetic Retinopathy Image Dataset (IDRID) (2019) https:\/\/ieee-dataport.org\/open-access\/indian-diabetic-retinopathy-image-datasetidrid"},{"key":"15576_CR69","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1016\/j.procs.2018.05.069","volume":"132","author":"S Indolia","year":"2018","unstructured":"Indolia S et al (2018) Conceptual understanding of convolutional neural network- a deep learning approach. Procedia Comput Sci 132:679\u2013688. ISSN: 1877-0509. https:\/\/doi.org\/10.1016\/j.procs.2018.05.069, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1877050918308019","journal-title":"Procedia Comput Sci"},{"key":"15576_CR70","doi-asserted-by":"crossref","first-page":"103361","DOI":"10.1016\/j.advengsoft.2022.103361","volume":"175","author":"S Inthiyaz","year":"2023","unstructured":"Inthiyaz S et al (2023) Skin disease detection using deep learning. Adv Eng Softw 175:103361","journal-title":"Adv Eng Softw"},{"key":"15576_CR71","unstructured":"Jammula R, Tejus VR, Shankar S (2020) Optimal transfer learning model for binary classification of funduscopic images through simple heuristics. arXiv:2002.04189[cs.LG]"},{"key":"15576_CR72","doi-asserted-by":"publisher","first-page":"115211","DOI":"10.1016\/j.eswa.2021.115211","volume":"182","author":"TJ Jun","year":"2021","unstructured":"Jun TJ et al (2021) TRk-CNN: Transferable ranking-CNN for image classification of glaucoma, glaucoma suspect, and normal eyes. Exp Syst Appl 182:115211. ISSN: 0957\u20134174. https:\/\/doi.org\/10.1016\/j.eswa.2021.115211, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0957417421006448","journal-title":"Exp Syst Appl"},{"key":"15576_CR73","doi-asserted-by":"publisher","first-page":"72726","DOI":"10.1109\/ACCESS.2019.2920448","volume":"7","author":"NM Khan","year":"2019","unstructured":"Khan NM, Abraham N, Hon M (2019) Transfer learning with intelligent training data selection for prediction of alzheimer\u2019s disease. IEEE Access 7:72726\u201372735. https:\/\/doi.org\/10.1109\/ACCESS.2019.2920448","journal-title":"IEEE Access"},{"issue":"12","key":"15576_CR74","doi-asserted-by":"crossref","first-page":"4267","DOI":"10.1109\/JBHI.2021.3067789","volume":"25","author":"MA Khan","year":"2021","unstructured":"Khan MA, Muhammad K, Sharif M, Akram T, de Albuquerque VHC (2021) Multi-class skin lesion detection and classification via teledermatology. IEEE J Biomed Health Inf 25(12):4267\u20134275. IEEE","journal-title":"IEEE J Biomed Health Inf"},{"key":"15576_CR75","doi-asserted-by":"crossref","first-page":"35941","DOI":"10.1007\/s11042-021-10551-6","volume":"80.28","author":"S-H Kim","year":"2021","unstructured":"Kim S-H, Koh HM, Lee B-D (2021) Classification of colorectal cancer in histological images using deep neural networks: An investigation. Multimed Tools Appl 80.28:35941\u201335953","journal-title":"Multimed Tools Appl"},{"key":"15576_CR76","doi-asserted-by":"crossref","first-page":"1919","DOI":"10.1007\/s10462-019-09722-7","volume":"53","author":"E Kozegar","year":"2020","unstructured":"Kozegar E, Soryani M, Behnam H, Salamati M, Tan T (2020) Computer aided detection in automated 3-D breast ultrasound images: a survey. Artif Intell Rev 53:1919\u20131941. Springer","journal-title":"Artif Intell Rev"},{"key":"15576_CR77","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet Classification with deep convolutional neural networks. In: Proceedings of the 25th International conference on neural information processing systems - vol 1. NIPS\u201912. Lake Tahoe, Nevada: Curran Associates Inc., pp 1097\u20131105"},{"key":"15576_CR78","doi-asserted-by":"publisher","first-page":"77725","DOI":"10.1109\/ACCESS.2020.2987961","volume":"8","author":"Y Kuang","year":"2020","unstructured":"Kuang Y, Lan T, Peng X, Selasi GE, Liu Q, Zhang J (2020) Unsupervised multi-discriminator generative adversarial network for lung nodule malignancy classification. IEEE Access 8:77725\u201377734. https:\/\/doi.org\/10.1109\/ACCESS.2020.2987961","journal-title":"IEEE Access"},{"key":"15576_CR79","doi-asserted-by":"publisher","unstructured":"Kumar M, Bansal M, Sachdeva M (2021) Transfer learning for image classification using VGG19: Caltech-101 image data set. Journal of Ambient Intelligence and Humanized Computing. https:\/\/doi.org\/10.1007\/s12652-021-03488-z","DOI":"10.1007\/s12652-021-03488-z"},{"key":"15576_CR80","doi-asserted-by":"publisher","first-page":"142521","DOI":"10.1109\/ACCESS.2020.3012292","volume":"8","author":"D Kumar","year":"2020","unstructured":"Kumar D et al (2020) Automatic detection of white blood cancer from bone marrow microscopic images using convolutional neural networks. IEEE Access 8:142521\u2013142531. https:\/\/doi.org\/10.1109\/ACCESS.2020.3012292","journal-title":"IEEE Access"},{"key":"15576_CR81","unstructured":"Labhsetwar SR et al (2020) Predictive analysis of diabetic retinopathy with transfer learning. arXiv:2011.04052[cs.CV]"},{"key":"15576_CR82","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86.11","author":"Y Lecun","year":"1998","unstructured":"Lecun Y et al (1998) Gradient-based learning applied to document recognition. Proc IEEE 86.11:2278\u20132324. https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proc IEEE"},{"key":"15576_CR83","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.jelectrocard.2019.11.046","volume":"58","author":"Z Li","year":"2020","unstructured":"Li Z, Zhou D, Wan L, Li J, Mou W (2020) Heartbeat classification using deep residual convolutional neural network from 2-lead electrocardiogram. J Electrocardiol 58:105\u2013112. ISSN: 0022-0736. https:\/\/doi.org\/10.1016\/j.jelectrocard.2019.11.046, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0022073619304170","journal-title":"J Electrocardiol"},{"key":"15576_CR84","doi-asserted-by":"publisher","first-page":"106849","DOI":"10.1016\/j.knosys.2021.106849","volume":"218","author":"C Li","year":"2021","unstructured":"Li C et al (2021) Transfer learning for establishment of recognition of COVID-19 on CT imaging using small-sized training datasets. Knowl-Based Syst 218:106849. ISSN: 0950-7051. https:\/\/doi.org\/10.1016\/j.knosys.2021.106849, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S095070512100112X","journal-title":"Knowl-Based Syst"},{"key":"15576_CR85","doi-asserted-by":"publisher","first-page":"898","DOI":"10.1109\/TMI.2019.2937271","volume":"39","author":"D Liang","year":"2020","unstructured":"Liang D, Sun L, Ma W, Paisley J (2020) A 3D spatially weighted network for segmentation of brain tissue from MRI. IEEE Trans Med Imaging 39:898\u2013909. https:\/\/doi.org\/10.1109\/TMI.2019.2937271","journal-title":"IEEE Trans Med Imaging"},{"key":"15576_CR86","doi-asserted-by":"publisher","first-page":"36188","DOI":"10.1109\/ACCESS.2018.2846685","volume":"6","author":"G Liang","year":"2018","unstructured":"Liang G et al (2018) Combining convolutional neural network with recursive neural network for blood cell image classification. IEEE Access 6:36188\u201336197. https:\/\/doi.org\/10.1109\/ACCESS.2018.2846685","journal-title":"IEEE Access"},{"key":"15576_CR87","doi-asserted-by":"publisher","unstructured":"Liu H, Huang KK, Ren CX, Lai ZR (2021) Hyperspectral image classification via discriminative convolutional neural network with an improved triplet loss. Pattern Recognition 112. https:\/\/doi.org\/10.1016\/j.patcog.2020.107744","DOI":"10.1016\/j.patcog.2020.107744"},{"key":"15576_CR88","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1049\/iet-ipr.2014.0311","volume":"9","author":"Y Liu","year":"2015","unstructured":"Liu Y, Wang W (2015) Simultaneous image fusion and denoising with adaptive sparse representation. IET Image Proc 9:347\u2013357. https:\/\/doi.org\/10.1049\/iet-ipr.2014.0311","journal-title":"IET Image Proc"},{"key":"15576_CR89","unstructured":"Liu X-J et al (2022) Few-shot learning for skin lesion image classification. Multimedia Tools and Applications, pp 1\u201312"},{"key":"15576_CR90","doi-asserted-by":"publisher","unstructured":"Ma Y, Niu D, Zhang J et al (2021) Unsupervised deformable image registration network for 3D medical images. Applied Intelligence springer. https:\/\/doi.org\/10.1007\/s10489-021-02196-7","DOI":"10.1007\/s10489-021-02196-7"},{"key":"15576_CR91","doi-asserted-by":"publisher","first-page":"4825","DOI":"10.3390\/app12104825","volume":"12","author":"R Mahmoudi","year":"2022","unstructured":"Mahmoudi R, Benameur N, Mabrouk R (2022) A Deep Learning-Based Diagnosis System for COVID-19 Detection and Pneumonia Screening Using CT Imaging. Appl Sci 12:4825. https:\/\/doi.org\/10.3390\/app12104825","journal-title":"Appl Sci"},{"key":"15576_CR92","doi-asserted-by":"publisher","first-page":"46278","DOI":"10.1109\/ACCESS.2019.2902252","volume":"7","author":"PK Mallick","year":"2019","unstructured":"Mallick PK et al (2019) Brain MRI image classification for cancer detection using deep wavelet autoencoder-based deep neural network. IEEE Access 7:46278\u201346287. https:\/\/doi.org\/10.1109\/ACCESS.2019.2902252","journal-title":"IEEE Access"},{"key":"15576_CR93","doi-asserted-by":"crossref","unstructured":"Mamalakis M et al (2021) DenResCov-19: A deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays. arXiv:2104.04006[eess.IV]","DOI":"10.1016\/j.compmedimag.2021.102008"},{"key":"15576_CR94","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1007\/s10489-014-0548-7","volume":"41","author":"EX Mart\u00edn","year":"2014","unstructured":"Mart\u00edn EX, Velasco M, Angulo C et al (2014) LTI ODE-valued neural networks. Appl Intell Springer 41:594\u2013605. https:\/\/doi.org\/10.1007\/s10489-014-0548-7","journal-title":"Appl Intell Springer"},{"key":"15576_CR95","unstructured":"Martinez AR (2020) Classification of COVID-19 in CT scans using multi-source transfer learning"},{"key":"15576_CR96","doi-asserted-by":"publisher","first-page":"87531","DOI":"10.1109\/ACCESS.2021.3074051","volume":"9","author":"S Masoudi","year":"2021","unstructured":"Masoudi S et al (2021) Deep Learning Based Staging of Bone Lesions From Computed Tomography Scans. IEEE Access 9:87531\u201387542. https:\/\/doi.org\/10.1109\/ACCESS.2021.3074051","journal-title":"IEEE Access"},{"key":"15576_CR97","doi-asserted-by":"publisher","first-page":"25657","DOI":"10.1109\/ACCESS.2022.3150924","volume":"10","author":"S Mehmood","year":"2022","unstructured":"Mehmood S et al (2022) Malignancy detection in lung and colon histopathology images using transfer learning with class selective image processing. IEEE Access 10:25657\u201325668. https:\/\/doi.org\/10.1109\/ACCESS.2022.3150924","journal-title":"IEEE Access"},{"key":"15576_CR98","unstructured":"Melanoma Cancer Cell Dataset (2020) https:\/\/sites.google.com\/view\/virginiafernandes\/datasets\/melanoma-cancer-cell-dataset."},{"key":"15576_CR99","doi-asserted-by":"publisher","first-page":"5804","DOI":"10.1109\/ACCESS.2017.2689058","volume":"5","author":"D Meng","year":"2017","unstructured":"Meng D et al (2017) Liver fibrosis classification based on transfer learning and FCNet for ultrasound images. IEEE Access 5:5804\u20135810. https:\/\/doi.org\/10.1109\/ACCESS.2017.2689058","journal-title":"IEEE Access"},{"key":"15576_CR100","doi-asserted-by":"publisher","first-page":"2091","DOI":"10.1109\/JBHI.2018.2878878","volume":"23.5","author":"N Meng","year":"2019","unstructured":"Meng N et al (2019) Large-scale multi-class image-based cell classification with deep learning. IEEE J Biomed Inform 23.5:2091\u20132098. https:\/\/doi.org\/10.1109\/JBHI.2018.2878878","journal-title":"IEEE J Biomed Inform"},{"key":"15576_CR101","doi-asserted-by":"crossref","unstructured":"Mercioni M-A, Stavarache LL (2022) Disease diagnosis with medical imaging using deep learning. In: Advances in information and communication: proceedings of the 2022 future of information and communication conference (FICC), vol 2. Springer, pp 198\u2013208","DOI":"10.1007\/978-3-030-98015-3_13"},{"key":"15576_CR102","doi-asserted-by":"crossref","first-page":"26255","DOI":"10.1007\/s11042-021-10952-7","volume":"80.17","author":"MM Mijwil","year":"2021","unstructured":"Mijwil MM (2021) Skin cancer disease images classification using deep learning solutions. Multimed Tools Appl 80.17:26255\u201326271","journal-title":"Multimed Tools Appl"},{"key":"15576_CR103","doi-asserted-by":"crossref","unstructured":"Motamed S, Rogalla P, Khalvati F (2020) RANDGAN: Randomized generative adversarial network for detection of COVID-19 in chest X-ray. arXiv:2010.06418[eess.IV]","DOI":"10.1038\/s41598-021-87994-2"},{"issue":"1","key":"15576_CR104","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1186\/s12874-022-01827-y","volume":"22","author":"S Moslehi","year":"2022","unstructured":"Moslehi S, Mahjub H, Farhadian M, Soltanian AR, Mamani M (2022) Interpretable generalized neural additive models for mortality prediction of COVID-19 hospitalized patients in Hamadan, Iran. BMC Med Res Methodol 22 (1):339. Springer","journal-title":"BMC Med Res Methodol"},{"key":"15576_CR105","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.slast.2021.10.011","volume":"27.1","author":"Z Mousavi","year":"2022","unstructured":"Mousavi Z et al (2022) COVID-19 detection using chest X-ray images based on a developed deep neural network. SLAS Technology 27.1:63\u201375. ISSN: 2472-6303. https:\/\/doi.org\/10.1016\/j.slast.2021.10.011, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S247263032100011X","journal-title":"SLAS Technology"},{"key":"15576_CR106","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.inffus.2021.02.013","volume":"72","author":"G Muhammad","year":"2021","unstructured":"Muhammad G, Shamim Hossain M (2021) COVID-19 and non-COVID-19 classification using multi-layers fusion from lung ultrasound images. Inf Fusion 72:80\u201388. ISSN: 1566-2535. https:\/\/doi.org\/10.1016\/j.inffus.2021.02.013, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1566253521000361","journal-title":"Inf Fusion"},{"key":"15576_CR107","unstructured":"NIH Chest X-ray Dataset (2018) https:\/\/www.kaggle.com\/nih-chest-xrays\/data"},{"key":"15576_CR108","unstructured":"NIH DeepLesion dataset (2018) https:\/\/www.kaggle.com\/kmader\/nih-deeplesion-subset."},{"key":"15576_CR109","doi-asserted-by":"publisher","first-page":"2592","DOI":"10.1109\/TPAMI.2013.96","volume":"35.11","author":"JC Nascimento","year":"2013","unstructured":"Nascimento JC, Carneiro G (2013) Combining Multiple Dynamic Models and Deep Learning Architectures for Tracking the Left Ventricle Endocardium in Ultrasound Data. IEEE Trans Pattern Anal Mach Intell 35.11:2592. https:\/\/doi.org\/10.1109\/TPAMI.2013.96","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"15576_CR110","doi-asserted-by":"publisher","first-page":"968","DOI":"10.1109\/TIP.2011.2169273","volume":"21.3","author":"JC Nascimento","year":"2012","unstructured":"Nascimento JC, Carneiro G, Freitas A (2012) The segmentation of the left ventricle of the heart from ultrasound data using deep learning architectures and derivative-based search methods. IEEE Trans Image Process 21.3:968\u2013982. https:\/\/doi.org\/10.1109\/TIP.2011.2169273","journal-title":"IEEE Trans Image Process"},{"key":"15576_CR111","doi-asserted-by":"publisher","first-page":"165724","DOI":"10.1109\/ACCESS.2019.2953318","volume":"7","author":"H Nasir Khan","year":"2019","unstructured":"Nasir Khan H et al (2019) Multi-view feature fusion based four views model for mammogram classification using convolutional neural network. IEEE Access 7:165724\u2013165733. https:\/\/doi.org\/10.1109\/ACCESS.2019.2953318","journal-title":"IEEE Access"},{"key":"15576_CR112","doi-asserted-by":"publisher","first-page":"114883","DOI":"10.1016\/j.eswa.2021.114883","volume":"176","author":"B Nigam","year":"2021","unstructured":"Nigam B et al (2021) COVID-19: Automatic detection from X-ray images by utilizing deep learning methods. Exp Syst Appl 176:114883. ISSN: 0957-4174. https:\/\/doi.org\/10.1016\/j.eswa.2021.114883, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0957417421003249","journal-title":"Exp Syst Appl"},{"key":"15576_CR113","doi-asserted-by":"publisher","first-page":"55135","DOI":"10.1109\/ACCESS.2020.2978629","volume":"8","author":"N Noreen","year":"2020","unstructured":"Noreen N et al (2020) A deep learning model based on concatenation approach for the diagnosis of brain tumor. IEEE Access 8:55135\u201355144. https:\/\/doi.org\/10.1109\/ACCESS.2020.2978629","journal-title":"IEEE Access"},{"key":"15576_CR114","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.patcog.2017.10.033","volume":"76","author":"JJ Pantrigo","year":"2018","unstructured":"Pantrigo JJ, Nunez JC, Cabido R, Montemayor AS (2018) Convolutional neural networks and long short-term memory for skeleton-based human activity and hand gesture recognition. Pattern Recognit 76:80\u201394. https:\/\/doi.org\/10.1016\/j.patcog.2017.10.033","journal-title":"Pattern Recognit"},{"key":"15576_CR115","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1016\/j.future.2020.09.020","volume":"115","author":"Y Peng","year":"2021","unstructured":"Peng Y, Zhu H, Han G, Zhao H (2021) Functional-realistic CT image super-resolution for early-stage pulmonary nodule detection. Future Gener Comput Syst 115:475\u2013485. https:\/\/doi.org\/10.1016\/j.future.2020.09.020","journal-title":"Future Gener Comput Syst"},{"key":"15576_CR116","doi-asserted-by":"publisher","unstructured":"Petrick N, Pezeshk A, Hamidian S, Sahiner B (2019) 3-D convolutional neural networks for automatic detection of pulmonary nodules in chest CT. In: IEEE Journal of biomedical and health informatics 23, pp 2080\u20132090. https:\/\/doi.org\/10.1109\/JBHI.2018.2879449","DOI":"10.1109\/JBHI.2018.2879449"},{"key":"15576_CR117","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.neucom.2020.07.102","volume":"419","author":"KM Poloni","year":"2021","unstructured":"Poloni KM et al (2021) Brain MR image classification for Alzheimer\u2019s disease diagnosis using structural hippocampal asymmetrical attributes from directional 3-D log-Gabor filter responses. Neurocomputing 419:126\u2013135. ISSN: 0925-2312. https:\/\/doi.org\/10.1016\/j.neucom.2020.07.102, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0925231220312972","journal-title":"Neurocomputing"},{"key":"15576_CR118","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.inffus.2019.07.004","volume":"54","author":"FJ Pulgar","year":"2020","unstructured":"Pulgar FJ, Charte F, Rivera AJ, del Jesus MJ (2020) Choosing the proper autoencoder for feature fusion based on data complexity and classifiers: Analysis, tips and guidelines. Inf Fusion 54:44\u201360. ISSN: 1566-2535. https:\/\/doi.org\/10.1016\/j.inffus.2019.07.004, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1566253519300880.","journal-title":"Inf Fusion"},{"key":"15576_CR119","doi-asserted-by":"publisher","unstructured":"Qureshi I, Shaheed K, Mao A, Zhang X (2022) Finger-vein presentation attack detection using depthwise separable convolution neural network. Expert Systems with Applications 198. https:\/\/doi.org\/10.1016\/j.eswa.2022.116786","DOI":"10.1016\/j.eswa.2022.116786"},{"key":"15576_CR120","doi-asserted-by":"crossref","first-page":"191586","DOI":"10.1109\/ACCESS.2020.3031384","volume":"8","author":"T Rahman","year":"2020","unstructured":"Rahman T, Khandakar A, Kadir MA, Islam KR, Islam KF, Mazhar R, Hamid T, Islam MT, Kashem S, Mahbub ZB et al (2020) Reliable tuberculosis detection using chest X-Ray with deep learning, segmentation and visualization. IEEE Access 8:191586\u2013191601. IEEE","journal-title":"IEEE Access"},{"key":"15576_CR121","doi-asserted-by":"publisher","first-page":"57810","DOI":"10.1109\/ACCESS.2020.2982588","volume":"8","author":"A Raj","year":"2020","unstructured":"Raj A, Shah NA, Tiwari AK, Martini MG (2020) Multivariate regression-based convolutional neural network model for fundus image quality assessment. IEEE Access 8:57810\u201357821. https:\/\/doi.org\/10.1109\/ACCESS.2020.2982588","journal-title":"IEEE Access"},{"key":"15576_CR122","doi-asserted-by":"publisher","first-page":"27318","DOI":"10.1109\/ACCESS.2020.2971257","volume":"8","author":"S Rajaraman","year":"2020","unstructured":"Rajaraman S, Antani SK (2020) Modality-specific deep learning model ensembles toward improving tb detection in chest radiographs. IEEE Access 8:27318\u201327326. https:\/\/doi.org\/10.1109\/ACCESS.2020.2971257","journal-title":"IEEE Access"},{"key":"15576_CR123","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2021\/6621607","volume":"2021","author":"AA Reshi","year":"2021","unstructured":"Reshi AA, Rustam F, Mehmood A, Alhossan A, Alrabiah Z, Ahmad A, Alsuwailem H, Choi GS (2021) An efficient CNN model for COVID-19 disease detection based on X-ray image classification. Complexity 2021:1\u201312. Hindawi Limited","journal-title":"Complexity"},{"key":"15576_CR124","unstructured":"Retinal OCT Images (optical coherence tomography) (2018) https:\/\/www.kaggle.com\/paultimothy,mooney\/kermany2018."},{"key":"15576_CR125","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1109\/JBHI.2018.2795545","volume":"23.1","author":"Y Rong","year":"2019","unstructured":"Rong Y et al (2019) surrogate-assisted retinal OCT image classification based on convolutional neural networks. IEEE J Biomed Health Inf 23.1:253\u2013263. https:\/\/doi.org\/10.1109\/JBHI.2018.2795545","journal-title":"IEEE J Biomed Health Inf"},{"key":"15576_CR126","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.1109\/TMI.2019.2947595","volume":"39","author":"RL Russell","year":"2020","unstructured":"Russell RL, Ozdemir O, Berlin AA (2020) A 3D probabilistic deep learning system for detection and diagnosis of lung cancer using low-dose CT scans. IEEE Trans Med Imaging 39:1419\u20131429. https:\/\/doi.org\/10.1109\/TMI.2019.2947595","journal-title":"IEEE Trans Med Imaging"},{"key":"15576_CR127","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.neunet.2020.09.004","volume":"132","author":"A Elazab","year":"2020","unstructured":"SJS Gardezi, Elazab A, Wang C, Bai H (2020) GP-GAN: Brain tumor growth prediction using stacked 3D generative adversarial networks from longitudinal MR Images. Neural Netw 132:321\u2013332. https:\/\/doi.org\/10.1016\/j.neunet.2020.09.004","journal-title":"Neural Netw"},{"key":"15576_CR128","doi-asserted-by":"crossref","unstructured":"Saha S, Sheikh N (2021) Ultrasound image classification using ACGAN with small training dataset. arXiv:2102.01539[eess.IV]","DOI":"10.1007\/978-981-33-6966-5_9"},{"key":"15576_CR129","doi-asserted-by":"publisher","unstructured":"Sakib S et al, Fouda MM, Fadlullah ZM, Guizani M (2020) DL-CRC: Deep learning-based chest radiograph classification for COVID-19 detection: A novel approach. IEEE Access 8:171575\u2013171589. https:\/\/doi.org\/10.1109\/ACCESS.2020.3025010","DOI":"10.1109\/ACCESS.2020.3025010"},{"issue":"23","key":"15576_CR130","doi-asserted-by":"crossref","first-page":"32705","DOI":"10.1007\/s11042-022-13005-9","volume":"81","author":"WM Salama","year":"2022","unstructured":"Salama WM, Shokry A, Aly MH (2022) A generalized framework for lung cancer classification based on deep generative models. Multimed Tools Applic 81(23):32705\u201332722. Springer","journal-title":"Multimed Tools Applic"},{"key":"15576_CR131","doi-asserted-by":"publisher","first-page":"1197","DOI":"10.1109\/TMI.2018.2881415","volume":"38.5","author":"H Salehinejad","year":"2019","unstructured":"Salehinejad H et al (2019) Synthesizing chest X-Ray pathology for training deep convolutional neural networks. IEEE Trans Med Imaging 38.5:1197\u20131206. https:\/\/doi.org\/10.1109\/TMI.2018.2881415","journal-title":"IEEE Trans Med Imaging"},{"key":"15576_CR132","unstructured":"Saxena A, Singh SP (2022) A deep learning approach for the detection of COVID-19 from chest X-Ray images using convolutional neural networks. https:\/\/europepmc.org\/article\/PPR\/PPR454232"},{"key":"15576_CR133","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/B978-0-12-409545-8.00001-7","volume-title":"Pattern recognition and signal analysis in medical imaging","author":"V Schmid","year":"2014","unstructured":"Schmid V, Meyer-Baese A (2014) Pattern recognition and signal analysis in medical imaging, 2nd edn. Academic Press, Cambridge, pp 1\u201320. https:\/\/doi.org\/10.1016\/B978-0-12-409545-8.00001-7","edition":"2nd edn."},{"key":"15576_CR134","doi-asserted-by":"publisher","first-page":"6566982","DOI":"10.1155\/2022\/6566982","volume":"2022","author":"S Shamim","year":"2022","unstructured":"Shamim S et al (2022) Automatic COVID-19 lung infection segmentation through modified Unet model. J Healthcare Eng 2022:6566982. https:\/\/doi.org\/10.1155\/2022\/6566982","journal-title":"J Healthcare Eng"},{"key":"15576_CR135","doi-asserted-by":"publisher","first-page":"132306","DOI":"10.1016\/j.physd.2019.132306","volume":"404","author":"A Sherstinsky","year":"2020","unstructured":"Sherstinsky A (2020) Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network. Phys D: Nonlinear Phenom 404:132306. ISSN: 0167-2789. https:\/\/doi.org\/10.1016\/j.physd.2019.132306","journal-title":"Phys D: Nonlinear Phenom"},{"key":"15576_CR136","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556[cs.CV]"},{"key":"15576_CR137","doi-asserted-by":"crossref","first-page":"1743","DOI":"10.1007\/s11042-021-11409-7","volume":"81.2","author":"S Singh","year":"2022","unstructured":"Singh S, Tripathi B (2022) Pneumonia classification using quaternion deep learning. Multimed Tools Appl 81.2:1743\u20131764","journal-title":"Multimed Tools Appl"},{"key":"15576_CR138","unstructured":"Skin Cancer MNIST: HAM10000 (2018) https:\/\/www.kaggle.com\/kmader\/skin-cancer-mnist-ham10000"},{"key":"15576_CR139","doi-asserted-by":"publisher","unstructured":"Soomro TA (2021) Artificial intelligence (AI) for medical imaging to combat coronavirus disease (COVID-19): a detailed review with direction for future research. Artificial Intelligence Review. ISSN: 1573-7462. https:\/\/doi.org\/10.1007\/s10462-021-09985-z","DOI":"10.1007\/s10462-021-09985-z"},{"key":"15576_CR140","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.eswa.2018.09.049","volume":"117","author":"PJ Sudharshan","year":"2019","unstructured":"Sudharshan PJ et al (2019) Multiple instance learning for histopathological breast cancer image classification. Exp Syst Appl 117:103\u2013111. ISSN: 0957-4174. https:\/\/doi.org\/10.1016\/j.eswa.2018.09.049, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0957417418306262","journal-title":"Exp Syst Appl"},{"key":"15576_CR141","doi-asserted-by":"publisher","first-page":"69215","DOI":"10.1109\/ACCESS.2019.2919122","volume":"7","author":"HH Sultan","year":"2019","unstructured":"Sultan HH, Salem NM, Al-Atabany W (2019) Multi-classification of brain tumor images using deep neural network. IEEE Access 7:69215\u201369225. https:\/\/doi.org\/10.1109\/ACCESS.2019.2919122","journal-title":"IEEE Access"},{"issue":"2","key":"15576_CR142","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1007\/s40123-022-00627-3","volume":"12","author":"G Sun","year":"2023","unstructured":"Sun G, Wang X, Xu L, Li C, Wang W, Yi Z, Luo H, Su Y, Zheng J, Li Z et al (2023) Deep learning for the detection of multiple fundus diseases using ultra-widefield images. Ophthalmol Therapy 12(2):895\u2013907. Springer","journal-title":"Ophthalmol Therapy"},{"key":"15576_CR143","doi-asserted-by":"publisher","unstructured":"Suresh S, Mohan S (2019) NROI based feature learning for automated tumor stage classification of pulmonary lung nodules using deep convolutional neural networks. Journal of King Saud University - Computer and Information Sciences. ISSN: 1319-1578. https:\/\/doi.org\/10.1016\/j.jksuci.2019.11.013, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S131915781931420X.","DOI":"10.1016\/j.jksuci.2019.11.013"},{"key":"15576_CR144","doi-asserted-by":"crossref","unstructured":"Szegedy C et al (2014) Going deeper with convolutions. arXiv:1409.4842[cs.CV]","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"15576_CR145","doi-asserted-by":"crossref","unstructured":"Szegedy C et al (2015) Rethinking the inception architecture for computer vision, arXiv:1512.00567[cs.CV]","DOI":"10.1109\/CVPR.2016.308"},{"key":"15576_CR146","doi-asserted-by":"crossref","unstructured":"Szegedy C et al (2016) Inception-v4, inception-ResNet and the impact of residual connections on learning. arXiv:1602.07261[cs.CV]","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"15576_CR147","unstructured":"The Cavy dataset (2016) http:\/\/www.inf-cv.uni-jena.de\/Research\/Datasets\/Cavy+Dataset.html."},{"key":"15576_CR148","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.eswa.2018.11.008","volume":"120","author":"FF Ting","year":"2019","unstructured":"Ting FF, Tan YJ, Sim KS (2019) Convolutional neural network improvement for breast cancer classification. Exp Syst Appl 120:103\u2013115. ISSN: 0957-4174. https:\/\/doi.org\/10.1016\/j.eswa.2018.11.008, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0957417418307280","journal-title":"Exp Syst Appl"},{"key":"15576_CR149","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1109\/JBHI.2018.2886276","volume":"23.3","author":"E Trivizakis","year":"2019","unstructured":"Trivizakis E et al (2019) Extending 2-D convolutional neural networks to 3-D for advancing deep learning cancer classification with application to MRI liver tumor differentiation. IEEE J Biomed Health Inf 23.3:923\u2013930. https:\/\/doi.org\/10.1109\/JBHI.2018.2886276","journal-title":"IEEE J Biomed Health Inf"},{"key":"15576_CR150","unstructured":"Tuberculosis (TB) Chest X-ray Database. 8.2 (2021) https:\/\/www.kaggle.com\/tawsifurrahman\/tuberculosis-tb-chest-xray-dataset"},{"key":"15576_CR151","doi-asserted-by":"publisher","unstructured":"Turkoglu M (2021) COVID-19 detection system using chest CT images and multiple kernels-extreme learning machine based on deep neural network. IRBM. ISSN: 1959-0318. https:\/\/doi.org\/10.1016\/j.irbm.2021.01.004, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1959031821000051","DOI":"10.1016\/j.irbm.2021.01.004"},{"key":"15576_CR152","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1049\/ipr2.12019","volume":"15","author":"S Vairamuthu","year":"2021","unstructured":"Vairamuthu S, Navaneethakrishnan M, Parthasarathy G (2021) Atom search-Jaya-based deep recurrent neural network for liver cancer detection. IET Image Proc 15:337\u2013349. https:\/\/doi.org\/10.1049\/ipr2.12019","journal-title":"IET Image Proc"},{"issue":"5","key":"15576_CR153","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1109\/TMI.2016.2526689","volume":"35","author":"MJJP van Grinsven","year":"2016","unstructured":"van Grinsven MJJP, van Ginneken B et al (2016) Fast convolutional neural network training using selective data sampling: application to hemorrhage detection in color fundus images. IEEE Trans Med Imaging 35(5):1273\u20131284. https:\/\/doi.org\/10.1109\/TMI.2016.2526689","journal-title":"IEEE Trans Med Imaging"},{"key":"15576_CR154","doi-asserted-by":"crossref","first-page":"91916","DOI":"10.1109\/ACCESS.2020.2994762","volume":"8","author":"A Waheed","year":"2020","unstructured":"Waheed A, Goyal M, Gupta D, Khanna A, Al-Turjman F, Pinheiro PR (2020) Covidgan: data augmentation using auxiliary classifier gan for improved covid-19 detection. IEEE Access 8:91916\u201391923. IEEE","journal-title":"IEEE Access"},{"key":"15576_CR155","doi-asserted-by":"publisher","first-page":"140767","DOI":"10.1109\/ACCESS.2020.3007599","volume":"8","author":"J Wang","year":"2020","unstructured":"Wang J (2020) OCT image recognition of cardiovascular vulnerable plaque based on CNN. IEEE Access 8:140767\u2013140776. https:\/\/doi.org\/10.1109\/ACCESS.2020.3007599","journal-title":"IEEE Access"},{"key":"15576_CR156","doi-asserted-by":"publisher","first-page":"4602","DOI":"10.1007\/s10489-020-01798-x","volume":"50","author":"SW Wang","year":"2020","unstructured":"Wang SW, Guo B, Y et al (2020) Twin labeled LDA: a supervised topic model for document classification. Appl Intell Springer 50:4602\u20134615. https:\/\/doi.org\/10.1007\/s10489-020-01798-x","journal-title":"Appl Intell Springer"},{"key":"15576_CR157","doi-asserted-by":"crossref","unstructured":"Wang Q, Li Y, Wang Y, Ren J (2022) An automatic algorithm for software vulnerability classification based on CNN and GRU. Multimedia Tools and Applications, pp 1\u201322","DOI":"10.1007\/s11042-022-12049-1"},{"key":"15576_CR158","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1049\/iet-ipr.2018.6057","volume":"13","author":"M Jiang","year":"2019","unstructured":"Wang M, Jiang M (2019) Deep residual refining based pseudo-multi-frame network for effective single image super-resolution. IET Image Process 13:591\u2013599. https:\/\/doi.org\/10.1049\/iet-ipr.2018.6057","journal-title":"IET Image Process"},{"key":"15576_CR159","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/JPHOT.2019.2934484","volume":"11.5","author":"D Wang","year":"2019","unstructured":"Wang D, Wang L (2019) On OCT Image Classification via Deep Learning. IEEE Photonics J 11.5:1\u201314. https:\/\/doi.org\/10.1109\/JPHOT.2019.2934484","journal-title":"IEEE Photonics J"},{"key":"15576_CR160","doi-asserted-by":"publisher","first-page":"134388","DOI":"10.1109\/ACCESS.2019.2941912","volume":"7","author":"Z Wang","year":"2019","unstructured":"Wang Z et al (2019) Dilated 3D Convolutional neural networks for brain MRI data classification. IEEE Access 7:134388\u2013134398. https:\/\/doi.org\/10.1109\/ACCESS.2019.2941912","journal-title":"IEEE Access"},{"key":"15576_CR161","doi-asserted-by":"publisher","first-page":"146533","DOI":"10.1109\/ACCESS.2019.2946000","volume":"7","author":"C Wang","year":"2019","unstructured":"Wang C et al (2019) Pulmonary image classification based on inception-v3 transfer learning model. IEEE Access 7:146533\u2013146541. https:\/\/doi.org\/10.1109\/ACCESS.2019.2946000","journal-title":"IEEE Access"},{"key":"15576_CR162","doi-asserted-by":"publisher","first-page":"146533","DOI":"10.1109\/ACCESS.2019.2946000","volume":"7","author":"C Wang","year":"2019","unstructured":"Wang C et al (2019) Pulmonary image classification based on inception-v3 transfer learning model. IEEE Access 7:146533\u2013146541. https:\/\/doi.org\/10.1109\/ACCESS.2019.2946000","journal-title":"IEEE Access"},{"key":"15576_CR163","doi-asserted-by":"publisher","first-page":"111874","DOI":"10.1016\/j.sna.2020.111874","volume":"307","author":"Y Wang","year":"2020","unstructured":"Wang Y et al (2020) An optimized deep convolutional neural network for dendrobium classification based on electronic nose. Sens Actuator A Phys 307:111874. ISSN: 0924-4247. https:\/\/doi.org\/10.1016\/j.sna.2020.111874, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0924424719303954","journal-title":"Sens Actuator A Phys"},{"key":"15576_CR164","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.inffus.2020.10.004","volume":"67","author":"S-H Wang","year":"2021","unstructured":"Wang S-H et al (2021) Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network. Inf Fusion 67:208\u2013229. ISSN:1566-2535. https:\/\/doi.org\/10.1016\/j.inffus.2020.10.004, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1566253520303705","journal-title":"Inf Fusion"},{"key":"15576_CR165","unstructured":"Wisconsin Breast Cancer Database (1992) https:\/\/archive.ics.uci.edu\/ml\/datasets\/breast+cancer+wisconsin+%28original%29"},{"key":"15576_CR166","doi-asserted-by":"publisher","first-page":"21400","DOI":"10.1109\/ACCESS.2019.2898044","volume":"7","author":"J Wu","year":"2019","unstructured":"Wu J, Li Y, Wu Q (2019) Classification of breast cancer histology images using multi-size and discriminative patches based on deep learning. IEEE Access 7:21400\u201321408. https:\/\/doi.org\/10.1109\/ACCESS.2019.2898044","journal-title":"IEEE Access"},{"key":"15576_CR167","doi-asserted-by":"publisher","first-page":"105873","DOI":"10.1016\/j.knosys.2020.105873","volume":"200","author":"Y Wu","year":"2020","unstructured":"Wu Y, Yi Z (2020) Automated detection of kidney abnormalities using multi-feature fusion convolutional neural networks. Knowl-Based Syst 200:105873. ISSN: 0950-7051. https:\/\/doi.org\/10.1016\/j.knosys.2020.105873, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0950705120302306","journal-title":"Knowl-Based Syst"},{"key":"15576_CR168","doi-asserted-by":"crossref","first-page":"106465","DOI":"10.1016\/j.knosys.2020.106465","volume":"208","author":"L Xie","year":"2020","unstructured":"Xie L, Zhang L, Hu T, Huang H, Yi Z (2020) Neural networks model based on an automated multi-scale method for mammogram classification. Knowl-Based Syst 208:106465. Elsevier","journal-title":"Knowl-Based Syst"},{"key":"15576_CR169","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1109\/TMI.2018.2876510","volume":"38.4","author":"Y Xie","year":"2019","unstructured":"Xie Y et al (2019) Knowledge-based collaborative deep learning for benign-malignant lung nodule classification on chest CT. IEEE Trans Med Imaging 38.4:991\u20131004. https:\/\/doi.org\/10.1109\/TMI.2018.2876510","journal-title":"IEEE Trans Med Imaging"},{"key":"15576_CR170","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.neucom.2021.03.034","volume":"443","author":"Y Xu","year":"2021","unstructured":"Xu Y, Lam H-K, Jia G (2021) MANet: A two-stage deep learning method for classification of COVID-19 from Chest X-ray images. Neurocomputing 443:96\u2013105. ISSN: 0925-2312. https:\/\/doi.org\/10.1016\/j.neucom.2021.03.034, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0925231221004021","journal-title":"Neurocomputing"},{"key":"15576_CR171","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1109\/TMI.2015.2458702","volume":"35.1","author":"J Xu","year":"2016","unstructured":"Xu J et al (2016) Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans Med Imaging 35.1:119\u2013130. https:\/\/doi.org\/10.1109\/TMI.2015.2458702","journal-title":"IEEE Trans Med Imaging"},{"key":"15576_CR172","doi-asserted-by":"publisher","first-page":"98693","DOI":"10.1109\/ACCESS.2020.2996217","volume":"8","author":"S Xu","year":"2020","unstructured":"Xu S et al (2020) Cxnet-M3: A Deep quintuplet network for multi-lesion classification in chest X-Ray images via multi-label supervision. IEEE Access 8:98693\u201398704. https:\/\/doi.org\/10.1109\/ACCESS.2020.2996217","journal-title":"IEEE Access"},{"key":"15576_CR173","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.neucom.2019.07.080","volume":"366","author":"Z Yang","year":"2019","unstructured":"Yang Z et al (2019) EMS-Net: Ensemble of multiscale convolutional neural networks for classification of breast cancer histology images. Neurocomputing 366:46\u201353. ISSN: 0925-2312. https:\/\/doi.org\/10.1016\/j.neucom.2019.07.080, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0925231219310872","journal-title":"Neurocomputing"},{"key":"15576_CR174","doi-asserted-by":"publisher","first-page":"84849","DOI":"10.1109\/ACCESS.2019.2925210","volume":"7","author":"X Yang","year":"2019","unstructured":"Yang X et al (2019) A two-stage convolutional neural network for pulmonary embolism detection from CTPA images. IEEE Access 7:84849\u201384857. https:\/\/doi.org\/10.1109\/ACCESS.2019.2925210","journal-title":"IEEE Access"},{"key":"15576_CR175","doi-asserted-by":"publisher","unstructured":"Yao R, Fan Y, Liu J, Yuan X (2021) COVID-19 detection from X-ray images using multi-kernel-size spatial-channel attention network. Pattern Recognit 119. https:\/\/doi.org\/10.1016\/j.patcog.2021.108055","DOI":"10.1016\/j.patcog.2021.108055"},{"key":"15576_CR176","doi-asserted-by":"publisher","first-page":"32559","DOI":"10.1109\/ACCESS.2021.3060447","volume":"9","author":"S Yu","year":"2021","unstructured":"Yu S et al (2021) Automatic classification of cervical cells using deep learning method. IEEE Access 9:32559\u201332568. https:\/\/doi.org\/10.1109\/ACCESS.2021.3060447","journal-title":"IEEE Access"},{"key":"15576_CR177","doi-asserted-by":"crossref","unstructured":"Zagoruyko S, Komodakis N (2017) Wide residual networks. arXiv:1605.07146[cs.CV]","DOI":"10.5244\/C.30.87"},{"key":"15576_CR178","doi-asserted-by":"crossref","unstructured":"Zeiler MD, Fergus R (2013) Visualizing and Understanding Convolutional Networks. arXiv:1311.2901[cs.CV]","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"15576_CR179","doi-asserted-by":"publisher","first-page":"133349","DOI":"10.1109\/ACCESS.2020.3010863","volume":"8","author":"B Zeimarani","year":"2020","unstructured":"Zeimarani B et al (2020) Breast lesion classification in ultrasound images using deep convolutional neural network. IEEE Access 8:133349\u2013133359. https:\/\/doi.org\/10.1109\/ACCESS.2020.3010863","journal-title":"IEEE Access"},{"key":"15576_CR180","doi-asserted-by":"publisher","first-page":"1633","DOI":"10.1109\/JBHI.2017.2705583","volume":"21.6","author":"L Zhang","year":"2017","unstructured":"Zhang L et al (2017) DeepPap: Deep convolutional networks for cervical cell classification. IEEE J Biomed Health Inf 21.6:1633\u20131643. https:\/\/doi.org\/10.1109\/JBHI.2017.2705583","journal-title":"IEEE J Biomed Health Inf"},{"key":"15576_CR181","doi-asserted-by":"publisher","first-page":"8659","DOI":"10.1109\/ACCESS.2021.3049600","volume":"9","author":"C Zhao","year":"2021","unstructured":"Zhao C et al (2021) Dermoscopy image classification based on StyleGAN and DenseNet201. IEEE Access 9:8659\u20138679. https:\/\/doi.org\/10.1109\/ACCESS.2021.3049600","journal-title":"IEEE Access"},{"key":"15576_CR182","doi-asserted-by":"publisher","first-page":"27917","DOI":"10.1109\/ACCESS.2022.3156096","volume":"10","author":"X Zhao","year":"2022","unstructured":"Zhao X et al (2022) Automatic thyroid ultrasound image classification using feature fusion network. IEEE Access 10:27917\u201327924. https:\/\/doi.org\/10.1109\/ACCESS.2022.3156096","journal-title":"IEEE Access"},{"key":"15576_CR183","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1016\/j.neunet.2019.09.009.","volume":"121","author":"L Zhou","year":"2020","unstructured":"Zhou L, Gu X (2020) Embedding topological features into convolutional neural network salient object detection. Neural Netw 121:308\u2013318. https:\/\/doi.org\/10.1016\/j.neunet.2019.09.009.","journal-title":"Neural Netw"},{"key":"15576_CR184","doi-asserted-by":"crossref","unstructured":"Zhou Q, Zhang J, Han G, Ruan Z, Wei Y (2022) Enhanced self-supervised GANs with blend ratio classification. Multimedia Tools and Applications, pp 1\u201317","DOI":"10.1007\/s11042-022-12056-2"},{"key":"15576_CR185","doi-asserted-by":"publisher","first-page":"17527","DOI":"10.1109\/ACCESS.2020.2967820","volume":"8","author":"L Zhou","year":"2020","unstructured":"Zhou L et al (2020) Transfer learning-based DCE-MRI method for identifying differentiation between benign and malignant breast tumors. IEEE Access 8:17527\u201317534. https:\/\/doi.org\/10.1109\/ACCESS.2020.2967820","journal-title":"IEEE Access"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15576-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-15576-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15576-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,15]],"date-time":"2024-02-15T10:18:28Z","timestamp":1707992308000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-15576-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,28]]},"references-count":185,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["15576"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-15576-7","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,28]]},"assertion":[{"value":"26 April 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 February 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 April 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 July 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no confict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}