{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T22:08:31Z","timestamp":1782425311010,"version":"3.54.5"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"30","license":[{"start":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T00:00:00Z","timestamp":1670803200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T00:00:00Z","timestamp":1670803200000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s00521-022-08084-6","type":"journal-article","created":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T11:02:59Z","timestamp":1670842979000},"page":"22115-22131","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["BF2SkNet: best deep learning features fusion-assisted framework for multiclass skin lesion classification"],"prefix":"10.1007","volume":"35","author":[{"given":"Muhammad","family":"Ajmal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Attique","family":"Khan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tallha","family":"Akram","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah","family":"Alqahtani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Majed","family":"Alhaisoni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ammar","family":"Armghan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sara A.","family":"Althubiti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fayadh","family":"Alenezi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,12,12]]},"reference":[{"key":"8084_CR1","doi-asserted-by":"crossref","first-page":"6300","DOI":"10.3390\/cancers13246300","volume":"13","author":"P Spyridonos","year":"2021","unstructured":"Spyridonos P, Gaitanis G, Likas A, Bassukas I (2021) Characterizing malignant melanoma clinically resembling seborrheic keratosis using deep knowledge transfer. Cancers 13:6300","journal-title":"Cancers"},{"key":"8084_CR2","doi-asserted-by":"crossref","first-page":"105725","DOI":"10.1016\/j.cmpb.2020.105725","volume":"197","author":"A Mahbod","year":"2020","unstructured":"Mahbod A, Tschandl P, Langs G, Ecker R, Ellinger I (2020) The effects of skin lesion segmentation on the performance of dermatoscopic image classification. Comput Methods Programs Biomed 197:105725","journal-title":"Comput Methods Programs Biomed"},{"key":"8084_CR3","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/j.mpmed.2021.04.010","volume":"49","author":"MK Lakhan","year":"2021","unstructured":"Lakhan MK, Lynch M (2021) Skin pigmentation. Medicine 49:447\u2013452","journal-title":"Medicine"},{"key":"8084_CR4","doi-asserted-by":"crossref","first-page":"101843","DOI":"10.1016\/j.compmedimag.2020.101843","volume":"88","author":"I Iqbal","year":"2021","unstructured":"Iqbal I, Younus M, Walayat K, Kakar MU, Ma J (2021) Automated multi-class classification of skin lesions through deep convolutional neural network with dermoscopic images. Comput Med Imaging Graph 88:101843","journal-title":"Comput Med Imaging Graph"},{"key":"8084_CR5","doi-asserted-by":"crossref","first-page":"105475","DOI":"10.1016\/j.cmpb.2020.105475","volume":"193","author":"A Mahbod","year":"2020","unstructured":"Mahbod A, Schaefer G, Wang C, Dorffner G, Ecker R, Ellinger I (2020) Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification. Comput Methods Programs Biomed 193:105475","journal-title":"Comput Methods Programs Biomed"},{"key":"8084_CR6","doi-asserted-by":"crossref","first-page":"12039","DOI":"10.1007\/s00521-021-05929-4","volume":"33","author":"O Sevli","year":"2021","unstructured":"Sevli O (2021) A deep convolutional neural network-based pigmented skin lesion classification application and experts evaluation. Neural Comput Appl 33:12039\u201312050","journal-title":"Neural Comput Appl"},{"key":"8084_CR7","doi-asserted-by":"crossref","unstructured":"Garg N, Sharma V, Kaur P (2018) Melanoma skin cancer detection using image processing. In: Sensors and image processing. Springer, pp 111\u2013119","DOI":"10.1007\/978-981-10-6614-6_12"},{"key":"8084_CR8","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1358\/dot.2005.41.1.875777","volume":"41","author":"RN Saladi","year":"2005","unstructured":"Saladi RN, Persaud AN (2005) The causes of skin cancer: a comprehensive review. Drugs Today 41:37\u201354","journal-title":"Drugs Today"},{"key":"8084_CR9","unstructured":"A. C. Society (2021) Melanoma of the skin. Cancer Statistics Centre. https:\/\/cancerstatisticscenter.cancer.org\/?_ga=2.121293320.1263676073.1630612387-522677707.1629187726#!\/cancer-site\/Melanoma%20of%20the%20skin"},{"key":"8084_CR10","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM et al (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542:115\u2013118","journal-title":"Nature"},{"key":"8084_CR11","doi-asserted-by":"crossref","first-page":"107502","DOI":"10.1016\/j.patcog.2020.107502","volume":"107","author":"L Bi","year":"2020","unstructured":"Bi L, Feng DD, Fulham M, Kim J (2020) Multi-label classification of multi-modality skin lesion via hyper-connected convolutional neural network. Pattern Recognit 107:107502","journal-title":"Pattern Recognit"},{"key":"8084_CR12","doi-asserted-by":"crossref","first-page":"e11936","DOI":"10.2196\/11936","volume":"20","author":"TJ Brinker","year":"2018","unstructured":"Brinker TJ, Hekler A, Utikal JS, Grabe N, Schadendorf D, Klode J et al (2018) Skin cancer classification using convolutional neural networks: systematic review. J Med Internet Res 20:e11936","journal-title":"J Med Internet Res"},{"key":"8084_CR13","doi-asserted-by":"crossref","unstructured":"Mahbod A, Schaefer G, Wang C, Ecker R, Dorffner G, Ellinger I (2021) Investigating and exploiting image resolution for transfer learning-based skin lesion classification. In: 2020 25th international conference on pattern recognition (ICPR), pp 4047\u20134053","DOI":"10.1109\/ICPR48806.2021.9412307"},{"key":"8084_CR14","doi-asserted-by":"crossref","unstructured":"Codella N, Cai J, Abedini M, Garnavi R, Halpern A, Smith JR (2015) Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images. In: International workshop on machine learning in medical imaging, pp 118\u2013126","DOI":"10.1007\/978-3-319-24888-2_15"},{"key":"8084_CR15","doi-asserted-by":"crossref","first-page":"114822","DOI":"10.1109\/ACCESS.2020.3003890","volume":"8","author":"MA Kassem","year":"2020","unstructured":"Kassem MA, Hosny KM, Fouad MM (2020) Skin lesions classification into eight classes for ISIC 2019 using deep convolutional neural network and transfer learning. IEEE Access 8:114822\u2013114832","journal-title":"IEEE Access"},{"key":"8084_CR16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2018.161","volume":"5","author":"P Tschandl","year":"2018","unstructured":"Tschandl P, Rosendahl C, Kittler H (2018) The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci Data 5:1\u20139","journal-title":"Sci Data"},{"key":"8084_CR17","doi-asserted-by":"crossref","first-page":"6095","DOI":"10.3390\/cancers13236095","volume":"13","author":"M Slowinska","year":"2021","unstructured":"Slowinska M, Kaminska-Winciorek G, Kowalska-Oledzka E, Czarnecka I, Czarnecki R, Nasierowska-Guttmejer A et al (2021) Dermoscopy of small diameter melanomas with the diagnostic feasibility of selected algorithms\u2014a clinical retrospective multicenter study. Cancers 13:6095","journal-title":"Cancers"},{"key":"8084_CR18","doi-asserted-by":"crossref","first-page":"5479","DOI":"10.3390\/ijerph18105479","volume":"18","author":"M Dildar","year":"2021","unstructured":"Dildar M, Akram S, Irfan M, Khan HU, Ramzan M, Mahmood AR et al (2021) Skin cancer detection: a review using deep learning techniques. Int J Environ Res Public Health 18:5479","journal-title":"Int J Environ Res Public Health"},{"key":"8084_CR19","doi-asserted-by":"crossref","first-page":"2771","DOI":"10.1001\/jama.292.22.2771","volume":"292","author":"NR Abbasi","year":"2004","unstructured":"Abbasi NR, Shaw HM, Rigel DS, Friedman RJ, McCarthy WH, Osman I et al (2004) Early diagnosis of cutaneous melanoma: revisiting the ABCD criteria. JAMA 292:2771\u20132776","journal-title":"JAMA"},{"key":"8084_CR20","doi-asserted-by":"crossref","first-page":"6048","DOI":"10.3390\/cancers13236048","volume":"13","author":"J Jaworek-Korjakowska","year":"2021","unstructured":"Jaworek-Korjakowska J, Brodzicki A, Cassidy B, Kendrick C, Yap MH (2021) Interpretability of a deep learning based approach for the classification of skin lesions into main anatomic body sites. Cancers 13:6048","journal-title":"Cancers"},{"key":"8084_CR21","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1007\/s00521-016-2482-6","volume":"29","author":"RB Oliveira","year":"2018","unstructured":"Oliveira RB, Papa JP, Pereira AS, Tavares JMR (2018) Computational methods for pigmented skin lesion classification in images: review and future trends. Neural Comput Appl 29:613\u2013636","journal-title":"Neural Comput Appl"},{"key":"8084_CR22","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.3390\/cancers13061192","volume":"13","author":"M Nishio","year":"2021","unstructured":"Nishio M, Nishio M, Jimbo N, Nakane K (2021) Homology-based image processing for automatic classification of histopathological images of lung tissue. Cancers 13:1192","journal-title":"Cancers"},{"key":"8084_CR23","first-page":"1","volume":"10","author":"Y Xu","year":"2020","unstructured":"Xu Y, Li Y, Shin B-S (2020) Medical image processing with contextual style transfer. HCIS 10:1\u201316","journal-title":"HCIS"},{"key":"8084_CR24","doi-asserted-by":"crossref","first-page":"105812","DOI":"10.1016\/j.compbiomed.2022.105812","volume":"148","author":"S-Y Lu","year":"2022","unstructured":"Lu S-Y, Wang S-H, Zhang Y-D (2022) SAFNet: a deep spatial attention network with classifier fusion for breast cancer detection. Comput Biol Med 148:105812","journal-title":"Comput Biol Med"},{"key":"8084_CR25","doi-asserted-by":"crossref","DOI":"10.1016\/j.csda.2019.106839","volume":"143","author":"A Bommert","year":"2020","unstructured":"Bommert A, Sun X, Bischl B, Rahnenf\u00fchrer J, Lang M (2020) Benchmark for filter methods for feature selection in high-dimensional classification data. Comput Stat Data Anal 143:106839","journal-title":"Comput Stat Data Anal"},{"key":"8084_CR26","doi-asserted-by":"crossref","unstructured":"Khan MA, Javed MY, Sharif M, Saba T, Rehman A (2019) Multi-model deep neural network based features extraction and optimal selection approach for skin lesion classification. In: 2019 international conference on computer and information sciences (ICCIS), pp 1\u20137","DOI":"10.1109\/ICCISci.2019.8716400"},{"key":"8084_CR27","doi-asserted-by":"crossref","first-page":"7503","DOI":"10.1007\/s00500-016-2305-9","volume":"21","author":"P Karczmarek","year":"2017","unstructured":"Karczmarek P, Pedrycz W, Kiersztyn A, Rutka P (2017) A study in facial features saliency in face recognition: an analytic hierarchy process approach. Soft Comput 21:7503\u20137517","journal-title":"Soft Comput"},{"key":"8084_CR28","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.3390\/biom10081123","volume":"10","author":"S Jinnai","year":"2020","unstructured":"Jinnai S, Yamazaki N, Hirano Y, Sugawara Y, Ohe Y, Hamamoto R (2020) The development of a skin cancer classification system for pigmented skin lesions using deep learning. Biomolecules 10:1123","journal-title":"Biomolecules"},{"key":"8084_CR29","doi-asserted-by":"crossref","unstructured":"O\u2019Mahony N, Campbell S, Carvalho A, Harapanahalli S, Hernandez GV, Krpalkova L et al (2019) Deep learning vs. traditional computer vision. In: Science and information conference, pp 128\u2013144","DOI":"10.1007\/978-3-030-17795-9_10"},{"key":"8084_CR30","doi-asserted-by":"crossref","first-page":"6116","DOI":"10.3390\/cancers13236116","volume":"13","author":"MF Mridha","year":"2021","unstructured":"Mridha MF, Hamid M, Monowar MM, Keya AJ, Ohi AQ, Islam M et al (2021) A comprehensive survey on deep-learning-based breast cancer diagnosis. Cancers 13:6116","journal-title":"Cancers"},{"key":"8084_CR31","first-page":"1","volume":"11","author":"S Bibi","year":"2021","unstructured":"Bibi S, Abbasi A, Haq IU, Baik SW, Ullah A (2021) Digital image forgery detection using deep autoencoder and CNN features. Hum Cent Comput Inf Sci 11:1\u201317","journal-title":"Hum Cent Comput Inf Sci"},{"key":"8084_CR32","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097\u20131105","journal-title":"Adv Neural Inf Process Syst"},{"key":"8084_CR33","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.compmedimag.2008.11.002","volume":"33","author":"ME Celebi","year":"2009","unstructured":"Celebi ME, Iyatomi H, Schaefer G, Stoecker WV (2009) Lesion border detection in dermoscopy images. Comput Med Imaging Graph 33:148\u2013153","journal-title":"Comput Med Imaging Graph"},{"key":"8084_CR34","doi-asserted-by":"crossref","first-page":"108743","DOI":"10.1016\/j.knosys.2022.108743","volume":"246","author":"RR Mostafa","year":"2022","unstructured":"Mostafa RR, Ewees AA, Ghoniem RM, Abualigah L, Hashim FA (2022) Boosting chameleon swarm algorithm with consumption AEO operator for global optimization and feature selection. Knowl-Based Syst 246:108743","journal-title":"Knowl-Based Syst"},{"key":"8084_CR35","doi-asserted-by":"crossref","first-page":"7437","DOI":"10.1007\/s00521-021-06751-8","volume":"34","author":"AA Ewees","year":"2022","unstructured":"Ewees AA, Mostafa RR, Ghoniem RM, Gaheen MA (2022) Improved seagull optimization algorithm using L\u00e9vy flight and mutation operator for feature selection. Neural Comput Appl 34:7437\u20137472","journal-title":"Neural Comput Appl"},{"key":"8084_CR36","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1049\/iet-cvi.2017.0193","volume":"12","author":"F Adjed","year":"2018","unstructured":"Adjed F, Gardezi SJS, Ababsa F, Faye I, Dass SC (2018) Fusion of structural and textural features for melanoma recognition. IET Comput Vis 12:185\u2013195","journal-title":"IET Comput Vis"},{"key":"8084_CR37","doi-asserted-by":"crossref","first-page":"1385","DOI":"10.1109\/JBHI.2019.2891049","volume":"23","author":"JR Hagerty","year":"2019","unstructured":"Hagerty JR, Stanley RJ, Almubarak HA, Lama N, Kasmi R, Guo P et al (2019) Deep learning and handcrafted method fusion: higher diagnostic accuracy for melanoma dermoscopy images. IEEE J Biomed Health Inform 23:1385\u20131391","journal-title":"IEEE J Biomed Health Inform"},{"key":"8084_CR38","unstructured":"Codella N, Rotemberg V, Tschandl P, Celebi ME, Dusza S, Gutman D et al (2019) Skin lesion analysis toward melanoma detection 2018: a challenge hosted by the international skin imaging collaboration (ISIC). arXiv preprint arXiv:1902.03368"},{"key":"8084_CR39","doi-asserted-by":"crossref","first-page":"23559","DOI":"10.1007\/s11042-019-7652-y","volume":"78","author":"Q Abbas","year":"2019","unstructured":"Abbas Q, Celebi ME (2019) DermoDeep-A classification of melanoma-nevus skin lesions using multi-feature fusion of visual features and deep neural network. Multimedia Tools Appl 78:23559\u201323580","journal-title":"Multimedia Tools Appl"},{"key":"8084_CR40","doi-asserted-by":"crossref","first-page":"102041","DOI":"10.1016\/j.bspc.2020.102041","volume":"62","author":"B Harangi","year":"2020","unstructured":"Harangi B, Baran A, Hajdu A (2020) Assisted deep learning framework for multi-class skin lesion classification considering a binary classification support. Biomed Signal Process Control 62:102041","journal-title":"Biomed Signal Process Control"},{"key":"8084_CR41","doi-asserted-by":"crossref","first-page":"2140","DOI":"10.1049\/iet-ipr.2019.1018","volume":"14","author":"F Xiao","year":"2020","unstructured":"Xiao F, Wu Q (2020) Visual saliency based global\u2013local feature representation for skin cancer classification. IET Image Proc 14:2140\u20132148","journal-title":"IET Image Proc"},{"key":"8084_CR42","doi-asserted-by":"crossref","first-page":"105568","DOI":"10.1016\/j.cmpb.2020.105568","volume":"195","author":"Z Qin","year":"2020","unstructured":"Qin Z, Liu Z, Zhu P, Xue Y (2020) A GAN-based image synthesis method for skin lesion classification. Comput Methods Programs Biomed 195:105568","journal-title":"Comput Methods Programs Biomed"},{"key":"8084_CR43","doi-asserted-by":"crossref","unstructured":"Afza F, Sharif M, Saba T, Rehman A, Javed MY (2020) Skin lesion classification: an optimized framework of optimal color features selection. In: 2020 2nd international conference on computer and information sciences (ICCIS), pp 1\u20136","DOI":"10.1109\/ICCIS49240.2020.9257667"},{"key":"8084_CR44","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1111\/1346-8138.15683","volume":"48","author":"HW Huang","year":"2020","unstructured":"Huang HW, Hsu BWY, Lee CH, Tseng VS (2020) Development of a light-weight deep learning model for cloud applications and remote diagnosis of skin cancers. J Dermatol 48:310\u2013316","journal-title":"J Dermatol"},{"key":"8084_CR45","doi-asserted-by":"crossref","first-page":"2870","DOI":"10.1109\/JBHI.2020.2977013","volume":"24","author":"P Tang","year":"2020","unstructured":"Tang P, Liang Q, Yan X, Xiang S, Zhang D (2020) GP-CNN-DTEL: global-part cnn model with data-transformed ensemble learning for skin lesion classification. IEEE J Biomed Health Inform 24:2870\u20132882","journal-title":"IEEE J Biomed Health Inform"},{"key":"8084_CR46","doi-asserted-by":"crossref","first-page":"101765","DOI":"10.1016\/j.compmedimag.2020.101765","volume":"84","author":"L Liu","year":"2020","unstructured":"Liu L, Mou L, Zhu XX, Mandal M (2020) Automatic skin lesion classification based on mid-level feature learning. Comput Med Imaging Graph 84:101765","journal-title":"Comput Med Imaging Graph"},{"key":"8084_CR47","doi-asserted-by":"crossref","first-page":"811","DOI":"10.3390\/diagnostics11060974","volume":"11","author":"M Sharif","year":"2021","unstructured":"Sharif M, Akram T, Dama\u0161evi\u010dius R, Maskeli\u016bnas R (2021) Skin lesion segmentation and multiclass classification using deep learning features and improved moth flame optimization. Diagnostics 11:811","journal-title":"Diagnostics"},{"key":"8084_CR48","doi-asserted-by":"crossref","unstructured":"Arshad M, Khan MA, Tariq U, Armghan A, Alenezi F, Younus Javed M et al (2021) A computer-aided diagnosis system using deep learning for multiclass skin lesion classification. Comput Intell Neurosci 2021","DOI":"10.1155\/2021\/9619079"},{"key":"8084_CR49","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.patrec.2020.12.015","volume":"143","author":"T Akram","year":"2021","unstructured":"Akram T, Zhang Y-D, Sharif M (2021) Attributes based skin lesion detection and recognition: A mask RCNN and transfer learning-based deep learning framework. Pattern Recognit Lett 143:58\u201366","journal-title":"Pattern Recognit Lett"},{"key":"8084_CR50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00521-021-05929-4","volume":"33","author":"O Sevli","year":"2021","unstructured":"Sevli O (2021) A deep convolutional neural network-based pigmented skin lesion classification application and experts evaluation. Neural Comput Appl 33:1\u201312","journal-title":"Neural Comput Appl"},{"key":"8084_CR51","first-page":"e12497","volume":"39","author":"MI Sharif","year":"2019","unstructured":"Sharif MI, Raza M, Anjum A, Saba T, Shad SA (2019) Skin lesion segmentation and classification: a unified framework of deep neural network features fusion and selection. Expert Syst 39:e12497","journal-title":"Expert Syst"},{"key":"8084_CR52","doi-asserted-by":"crossref","unstructured":"Miko\u0142ajczyk A, Grochowski M (2018) Data augmentation for improving deep learning in image classification problem. In: 2018 international interdisciplinary PhD workshop (IIPhDW), pp 117\u2013122","DOI":"10.1109\/IIPHDW.2018.8388338"},{"key":"8084_CR53","doi-asserted-by":"crossref","first-page":"106956","DOI":"10.1016\/j.compeleceng.2020.106956","volume":"90","author":"Y-D Zhang","year":"2021","unstructured":"Zhang Y-D, Sharif M, Akram T (2021) Pixels to classes: intelligent learning framework for multiclass skin lesion localization and classification. Comput Electr Eng 90:106956","journal-title":"Comput Electr Eng"},{"key":"8084_CR54","first-page":"1","volume":"45","author":"N Hussain","year":"2020","unstructured":"Hussain N, Khan MA, Sharif M, Khan SA, Albesher AA, Saba T et al (2020) A deep neural network and classical features based scheme for objects recognition: an application for machine inspection. Multimedia Tools Appl 45:1\u201323","journal-title":"Multimedia Tools Appl"},{"key":"8084_CR55","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15:1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"8084_CR56","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-016-0043-6","volume":"3","author":"K Weiss","year":"2016","unstructured":"Weiss K, Khoshgoftaar TM, Wang D (2016) A survey of transfer learning. J Big Data 3:1\u201340","journal-title":"J Big Data"},{"key":"8084_CR57","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.ins.2018.07.074","volume":"467","author":"T Akram","year":"2018","unstructured":"Akram T, Laurent B, Naqvi SR, Alex MM, Muhammad N (2018) A deep heterogeneous feature fusion approach for automatic land-use classification. Inf Sci 467:199\u2013218","journal-title":"Inf Sci"},{"key":"8084_CR58","doi-asserted-by":"crossref","unstructured":"Zoph B, Vasudevan V, Shlens J, Le QV (2018) Learning transferable architectures for scalable image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8697\u20138710","DOI":"10.1109\/CVPR.2018.00907"},{"key":"8084_CR59","doi-asserted-by":"crossref","first-page":"7584","DOI":"10.3390\/s21227584","volume":"21","author":"F Saleem","year":"2021","unstructured":"Saleem F, Khan MA, Alhaisoni M, Tariq U, Armghan A, Alenezi F et al (2021) Human gait recognition: a single stream optimal deep learning features fusion. Sensors 21:7584","journal-title":"Sensors"},{"key":"8084_CR60","doi-asserted-by":"crossref","unstructured":"Howard FL (1931) The life history of Physarum polycephalum. Am J Bot 116\u2013133","DOI":"10.1002\/j.1537-2197.1931.tb09577.x"},{"key":"8084_CR61","doi-asserted-by":"crossref","unstructured":"Camp WG (1936) A method of cultivating myxomycete plasmodia. Bull Torrey Bot Club 205\u2013210","DOI":"10.2307\/2480903"},{"key":"8084_CR62","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.future.2020.03.055","volume":"111","author":"S Li","year":"2020","unstructured":"Li S, Chen H, Wang M, Heidari AA, Mirjalili S (2020) Slime mould algorithm: a new method for stochastic optimization. Futur Gener Comput Syst 111:300\u2013323","journal-title":"Futur Gener Comput Syst"},{"key":"8084_CR63","first-page":"1","volume":"78","author":"K Muhammad","year":"2021","unstructured":"Muhammad K, Sharif M, Akram T, Kadry S (2021) Intelligent fusion-assisted skin lesion localization and classification for smart healthcare. Neural Comput Appl 78:1\u201316","journal-title":"Neural Comput Appl"},{"key":"8084_CR64","first-page":"1041","volume":"68","author":"T Akram","year":"2021","unstructured":"Akram T, Sharif M, Kadry S, Nam Y (2021) Computer decision support system for skin cancer localization and classification. Comput Mater Contin 68:1041\u20131064","journal-title":"Comput Mater Contin"},{"key":"8084_CR65","unstructured":"Sharif M, Akram T, Kadry S, Hsu CH (2021) A two\u2010stream deep neural network\u2010based intelligent system for complex skin cancer types classification. Int J Intell Syst"},{"key":"8084_CR66","doi-asserted-by":"crossref","first-page":"109434","DOI":"10.1016\/j.knosys.2022.109434","volume":"252","author":"L Deng","year":"2022","unstructured":"Deng L, Wang S-H, Zhang Y-D (2022) ELMGAN: A GAN-based efficient lightweight multi-scale-feature-fusion multi-task model. Knowl-Based Syst 252:109434","journal-title":"Knowl-Based Syst"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-08084-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-08084-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-08084-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T15:02:43Z","timestamp":1694876563000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-08084-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,12]]},"references-count":66,"journal-issue":{"issue":"30","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["8084"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-08084-6","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,12]]},"assertion":[{"value":"8 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 November 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 December 2022","order":3,"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 competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}