{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T15:08:46Z","timestamp":1778857726787,"version":"3.51.4"},"reference-count":69,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,3,31]],"date-time":"2020-03-31T00:00:00Z","timestamp":1585612800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,3,31]],"date-time":"2020-03-31T00:00:00Z","timestamp":1585612800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100008809","name":"University of Hail","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100008809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Hum. Cent. Comput. Inf. Sci."],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Melanoma is considered to be one of the deadliest skin cancer types, whose occurring frequency elevated in the last few years; its earlier diagnosis, however, significantly increases the chances of patients\u2019 survival. In the quest for the same, a few computer based methods, capable of diagnosing the skin lesion at initial stages, have been recently proposed. Despite some success, however, margin exists, due to which the machine learning community still considers this an outstanding research challenge. In this work, we come up with a novel framework for skin lesion classification, which integrates deep features information to generate most discriminant feature vector, with an advantage of preserving the original feature space. We utilize recent deep models for feature extraction, and by taking advantage of transfer learning. Initially, the dermoscopic images are segmented, and the lesion region is extracted, which is later subjected to retrain the selected deep models to generate fused feature vectors. In the second phase, a framework for most discriminant feature selection and dimensionality reduction is proposed, entropy-controlled neighborhood component analysis (ECNCA). This hierarchical framework optimizes fused features by selecting the principle components and extricating the redundant and irrelevant data. The effectiveness of our design is validated on four benchmark dermoscopic datasets; PH2, ISIC MSK, ISIC UDA, and ISBI-2017. To authenticate the proposed method, a fair comparison with the existing techniques is also provided. The simulation results clearly show that the proposed design is accurate enough to categorize the skin lesion with 98.8%, 99.2% and 97.1% and 95.9% accuracy with the selected classifiers on all four datasets, and by utilizing less than 3% features.<\/jats:p>","DOI":"10.1186\/s13673-020-00216-y","type":"journal-article","created":{"date-parts":[[2020,3,31]],"date-time":"2020-03-31T14:03:01Z","timestamp":1585663381000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["A multilevel features selection framework for skin lesion classification"],"prefix":"10.1186","volume":"10","author":[{"given":"Tallha","family":"Akram","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hafiz M. Junaid","family":"Lodhi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Syed Rameez","family":"Naqvi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sidra","family":"Naeem","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Majed","family":"Alhaisoni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad","family":"Ali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sajjad Ali","family":"Haider","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nadia N.","family":"Qadri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,3,31]]},"reference":[{"key":"216_CR1","unstructured":"Skin cancer facts, 2017. URL https:\/\/seer.cancer.gov\/statfacts\/html\/melan.html"},{"key":"216_CR2","first-page":"965","volume":"8","author":"C Barata","year":"2014","unstructured":"Barata C, Ruela M, Francisco M, Mendonca T, Marques J (2014) Two systems for the detection of melanomas in dermoscopy images using texture and color features. Syst J 8:965\u2013979","journal-title":"Syst J"},{"key":"216_CR3","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.procs.2014.11.029","volume":"42","author":"AN Hoshyar","year":"2014","unstructured":"Hoshyar AN, Al-Jumaily A (2014) The beneficial techniques in preprocessing step of skin cancer detection system comparing. Procedia Comput Sci 42:25\u201331","journal-title":"Procedia Comput Sci"},{"key":"216_CR4","first-page":"521","volume":"4","author":"F Nachbar","year":"1994","unstructured":"Nachbar F, Stolz W, Merkle T, Cognetta AB, Vogt T, Landthaler M, Bilek P, Braunfalco O, Plewig G (1994) The ABCD rule of dermatoscopy. J Am Acad Dermatol 4:521\u2013527","journal-title":"J Am Acad Dermatol"},{"key":"216_CR5","first-page":"1563","volume":"134","author":"M Delfino","year":"1998","unstructured":"Delfino M, Argenziano G, Fabbrocini G, Carli P, Giorgi VD, Sammarco E (1998) Epiluminescence microscopy for the diagnosis of doubtful melanocytic skin lesions. Comparison of the ABCD rule. Arch Dermatol 134:1563\u20131570","journal-title":"Arch Dermatol"},{"key":"216_CR6","doi-asserted-by":"publisher","first-page":"1178","DOI":"10.1001\/archderm.1996.03890340038007","volume":"132","author":"SW Menzies","year":"1996","unstructured":"Menzies SW, Ingvar C, Crotty KA, McCarthy WH (1996) Frequency and morphologic characteristics of invasive melanomas lacking specific surface microscopic features. Arch Dermatol 132:1178\u20131182","journal-title":"Arch Dermatol"},{"key":"216_CR7","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1067\/mjd.2003.281","volume":"48","author":"G Argenziano","year":"2003","unstructured":"Argenziano G, Soyer HP, Chimenti S, Talamini R, Corona R, Binder M, Sera F, Cerroni L, De Rosa G, Ferrara G (2003) Dermoscopy of pigmented skin lesions: results of a consensus meeting via the internet. J Am Acad Dermatol 48:679\u2013693","journal-title":"J Am Acad Dermatol"},{"key":"216_CR8","doi-asserted-by":"crossref","unstructured":"Ruela M, Barata C, Mendonca T, Marques J (2013) On the role of shape in the detection of melanomas. In: 8th international symposium on image and signal processing and analysis (ISPA 2013)","DOI":"10.1109\/ISPA.2013.6703751"},{"key":"216_CR9","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.ijmedinf.2019.01.005","volume":"124","author":"N Nida","year":"2019","unstructured":"Nida N, Irtaza A, Javed A, Yousaf MH, Mahmood MT (2019) Melanoma lesion detection and segmentation using deep region based convolutional neural network and fuzzy C-means clustering. Int J Med Inform 124:37\u201348","journal-title":"Int J Med Inform"},{"issue":"3","key":"216_CR10","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1007\/s11263-014-0700-1","volume":"108","author":"B Fernando","year":"2014","unstructured":"Fernando B, Fromont E, Tuytelarrs T (2014) Mining mid-level features for image classification. J Comput Vis 108(3):186\u2013203","journal-title":"J Comput Vis"},{"key":"216_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-018-1051-5","author":"T Akram","year":"2018","unstructured":"Akram T, Khan MA, Sharif M, Yasmin M (2018) Skin lesion segmentation and recognition using multichannel saliency estimation and M-SVM on selected serially fused features. J Ambient Intell Humaniz Comput. https:\/\/doi.org\/10.1007\/s12652-018-1051-5","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"216_CR12","doi-asserted-by":"publisher","first-page":"113196","DOI":"10.1016\/j.eswa.2020.113196","volume":"149","author":"A Khatami","year":"2020","unstructured":"Khatami A, Nazari A, Khosravi A, Lim CP, Nahavandi S (2020) A weight perturbation-based regularisation technique for convolutional neural networks and the application in medical imaging. Expert Syst Appl 149:113196","journal-title":"Expert Syst Appl"},{"issue":"2","key":"216_CR13","doi-asserted-by":"publisher","first-page":"556","DOI":"10.3390\/s18020556","volume":"18","author":"Y Li","year":"2018","unstructured":"Li Y, Shen L (2018) Skin lesion analysis towards melanoma detection using deep learning network. Sensors 18(2):556","journal-title":"Sensors"},{"key":"216_CR14","doi-asserted-by":"publisher","first-page":"101660","DOI":"10.1016\/j.compmedimag.2019.101660","volume":"79","author":"J Dolz","year":"2020","unstructured":"Dolz J, Desrosiers C, Wang L, Yuan J, Shen D, Ayed IB (2020) Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation. Comput Med Imaging Gr 79:101660","journal-title":"Comput Med Imaging Gr"},{"issue":"9","key":"216_CR15","doi-asserted-by":"publisher","first-page":"2065","DOI":"10.1109\/TBME.2017.2712771","volume":"64","author":"L Bi","year":"2017","unstructured":"Bi L, Kim J, Ahn E, Kumar A, Fulham M, Feng D (2017) Dermoscopic image segmentation via multi-stage fully convolutional networks. IEEE Trans Biomed Eng 64(9):2065\u20132074","journal-title":"IEEE Trans Biomed Eng"},{"key":"216_CR16","doi-asserted-by":"crossref","unstructured":"Marques JS, Barata C, Mendonca T (2012) On the role of texture and color in the classification of dermoscopy images. In: Annual international conference of the IEEE engineering in medicine and biology society (EMBC)","DOI":"10.1109\/EMBC.2012.6346942"},{"issue":"3","key":"216_CR17","first-page":"233","volume":"20","author":"H Ganster","year":"2001","unstructured":"Ganster H, Pinz A, Rohrer R, Wildling E, Blinder M, Kittler H (2001) Automated melanoma recognition. IEEE Trans Biom Eng 20(3):233\u2013239","journal-title":"IEEE Trans Biom Eng"},{"issue":"1","key":"216_CR18","doi-asserted-by":"publisher","first-page":"638","DOI":"10.1186\/s12885-018-4465-8","volume":"18","author":"MA Khan","year":"2018","unstructured":"Khan MA, Tallha A, Muhammad S, Aamir S, Khursheed A, Musaed A, Syed IH, Abdualziz A (2018) An implementation of normal distribution based segmentation and entropy-controlled features selection for skin lesion detection and classification. BMC Cancer 18(1):638","journal-title":"BMC Cancer"},{"key":"216_CR19","doi-asserted-by":"crossref","unstructured":"Naeem S, Riaz F, Hassan A, Miguel Tavares C, Nisar R (2015) Description of visual content in dermoscopy images using joint histogram of multiresolution local binary patterns and local contrast. In: Proceedings of 16th international conference on intelligent data engineering and automated learning (IDEAL 2015), Poland","DOI":"10.1007\/978-3-319-24834-9_50"},{"key":"216_CR20","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1016\/j.patrec.2019.11.034","volume":"129","author":"MA Khan","year":"2020","unstructured":"Khan MA, Sharif M, Akram T, Bukhari SA, Nayak RS (2020) Developed newton-raphson based deep features selection framework for skin lesion recognition. Pattern Recognit Lett 129:293\u2013303","journal-title":"Pattern Recognit Lett"},{"key":"216_CR21","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.procs.2015.03.090","volume":"45","author":"R Sumithra","year":"2015","unstructured":"Sumithra R, Suhil M, Guru DS (2015) Segmentation and classification of skin lesions for disease diagnosis. Procedia Comput Sci 45:76\u201385","journal-title":"Procedia Comput Sci"},{"key":"216_CR22","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.cmpb.2019.05.010","volume":"177","author":"M Attia","year":"2019","unstructured":"Attia M, Hossny M, Zhou H, Nahavandi S, Asadi H, Yazdabadi A (2019) Digital hair segmentation using hybrid convolutional and recurrent neural networks architecture. Comput Methods Progr Biomed 177:17\u201330","journal-title":"Comput Methods Progr Biomed"},{"key":"216_CR23","doi-asserted-by":"crossref","unstructured":"Joseph S, Panicker JR (2016) Skin lesion analysis system for melanoma detection with an effective hair segmentation method. In: International conference on information science (ICIS). IEEE, New York, pp 91\u201396","DOI":"10.1109\/INFOSCI.2016.7845307"},{"issue":"2","key":"216_CR24","first-page":"9164","volume":"3","author":"N Cheerla","year":"2014","unstructured":"Cheerla N, Frazier D (2014) Automatic melanoma detection using multi-stage neural networks. Int J Innov Res Sci Eng Technol 3(2):9164\u20139183","journal-title":"Int J Innov Res Sci Eng Technol"},{"key":"216_CR25","doi-asserted-by":"publisher","first-page":"112895","DOI":"10.1016\/j.eswa.2019.112895","volume":"140","author":"KA Khan","year":"2020","unstructured":"Khan KA, Shanir PP, Khan YU, Farooq O (2020) A hybrid local binary pattern and wavelets based approach for EEG classification for diagnosing epilepsy. Expert Syst Appl 140:112895","journal-title":"Expert Syst Appl"},{"key":"216_CR26","doi-asserted-by":"publisher","first-page":"105931","DOI":"10.1016\/j.asoc.2019.105931","volume":"86","author":"AR Hawas","year":"2020","unstructured":"Hawas AR, Guo Y, Du C, Polat K, Ashour AS (2020) OCE-NGC: a neutrosophic graph cut algorithm using optimized clustering estimation algorithm for dermoscopic skin lesion segmentation. Appl Soft Comput 86:105931","journal-title":"Appl Soft Comput"},{"key":"216_CR27","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.tcs.2020.01.016","volume":"814","author":"M Hajiaghayi","year":"2020","unstructured":"Hajiaghayi M, Kortsarz G, MacDavid R, Purohit M, Sarpatwar K (2020) Approximation algorithms for connected maximum cut and related problems. Theor Comput Sci 814:74\u201385","journal-title":"Theor Comput Sci"},{"key":"216_CR28","doi-asserted-by":"publisher","first-page":"113129","DOI":"10.1016\/j.eswa.2019.113129","volume":"144","author":"MP Pour","year":"2020","unstructured":"Pour MP, Seker H (2020) Transform domain representation-driven convolutional neural networks for skin lesion segmentation. Expert Syst Appl 144:113129","journal-title":"Expert Syst Appl"},{"key":"216_CR29","doi-asserted-by":"crossref","unstructured":"Ahn E, Bi L, Jung YH, Kim J, Li C, Fulham M, Feng DD (2015) Automated saliency-based lesion segmentation in dermoscopic images. In: 2015 37th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, New York, pp 3009-3012","DOI":"10.1109\/EMBC.2015.7319025"},{"key":"216_CR30","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04514-0","author":"MA Khan","year":"2019","unstructured":"Khan MA, Akram T, Sharif M, Javed K, Rashid M, Bukhari SAC (2019) An integrated framework of skin lesion detection and recognition through saliency method and optimal deep neural network features selection. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-019-04514-0","journal-title":"Neural Comput Appl"},{"issue":"3","key":"216_CR31","doi-asserted-by":"publisher","first-page":"965","DOI":"10.1109\/JSYST.2013.2271540","volume":"8","author":"C Barata","year":"2014","unstructured":"Barata C, Ruela M, Francisco M, Mendona T, Marques JS (2014) Two systems for the detection of melanomas in dermoscopy images using texture and color features. IEEE Syst J 8(3):965\u2013979","journal-title":"IEEE Syst J"},{"issue":"1","key":"216_CR32","doi-asserted-by":"publisher","first-page":"e490","DOI":"10.1111\/j.1600-0846.2012.00670.x","volume":"19","author":"A Qaisar","year":"2013","unstructured":"Qaisar A, Garcia IF, Emre Celebi M, Ahmad W, Mushtaq Q (2013) A perceptually oriented method for contrast enhancement and segmentation of dermoscopy images. Skin Res Technol 19(1):e490\u2013e497","journal-title":"Skin Res Technol"},{"key":"216_CR33","doi-asserted-by":"publisher","first-page":"103190","DOI":"10.1016\/j.jbi.2019.103190","volume":"94","author":"G Nagarajan","year":"2019","unstructured":"Nagarajan G, Babu LD (2019) A hybrid of whale optimization and late acceptance hill climbing based imputation to enhance classification performance in electronic health records. J Biomed Inform 94:103190","journal-title":"J Biomed Inform"},{"key":"216_CR34","doi-asserted-by":"publisher","first-page":"101581","DOI":"10.1016\/j.bspc.2019.101581","volume":"53","author":"S Chatterjee","year":"2019","unstructured":"Chatterjee S, Dey D, Munshi S, Gorai S (2019) Extraction of features from cross correlation in space and frequency domains for classification of skin lesions. Biomed Signal Process Control 53:101581","journal-title":"Biomed Signal Process Control"},{"key":"216_CR35","doi-asserted-by":"crossref","unstructured":"Bi L, Kim J, Ahn E, Feng D, Fulham M (2016) Automatic melanoma detection via multi-scale lesion-biased representation and joint reverse classification. In: 2016 IEEE 13th international symposium on biomedical imaging (ISBI). IEEE, New York, pp 1055\u20131058","DOI":"10.1109\/ISBI.2016.7493447"},{"key":"216_CR36","doi-asserted-by":"crossref","unstructured":"Abuzaghleh O, Faezipour M, Barkana BD (2016) A portable real-time noninvasice skin lesion analysis system to assist in melanoma early detection and prevention","DOI":"10.1109\/LISAT.2015.7160183"},{"key":"216_CR37","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.compmedimag.2018.10.007","volume":"71","author":"A Mahbod","year":"2018","unstructured":"Mahbod A, Schaefer G, Ellinger I, Ecker R, Pitiot A, Wang C (2018) Fusing fine-tuned deep features for skin lesion classification. Comput Med Imaging Gr 71:19","journal-title":"Comput Med Imaging Gr"},{"key":"216_CR38","doi-asserted-by":"publisher","first-page":"105351","DOI":"10.1016\/j.cmpb.2020.105351","volume":"190","author":"MA Al-masni","year":"2020","unstructured":"Al-masni MA, Kim DH, Kim TS (2020) Multiple skin lesions diagnostics via integrated deep convolutional networks for segmentation and classification. Comput Methods Progr Biomed 190:105351","journal-title":"Comput Methods Progr Biomed"},{"key":"216_CR39","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","volume":"121","author":"N Ibtehaz","year":"2020","unstructured":"Ibtehaz N, Rahman MS (2020) MultiResuNet: rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Netw 121:74\u201387","journal-title":"Neural Netw"},{"key":"216_CR40","doi-asserted-by":"publisher","first-page":"101792","DOI":"10.1016\/j.bspc.2019.101792","volume":"57","author":"M Hajabdollahi","year":"2020","unstructured":"Hajabdollahi M, Esfandiarpoor R, Sabeti E, Karimi N, Soroushmehr SM, Samavi S (2020) Multiple abnormality detection for automatic medical image diagnosis using bifurcated convolutional neural network. Biomed Signal Process Control 57:101792","journal-title":"Biomed Signal Process Control"},{"key":"216_CR41","doi-asserted-by":"publisher","first-page":"100282","DOI":"10.1016\/j.imu.2019.100282","volume":"18","author":"MA Kadampur","year":"2020","unstructured":"Kadampur MA, Al Riyaee S (2020) Skin cancer detection: applying a deep learning based model driven architecture in the cloud for classifying dermal cell images. Inform Med Unlocked 18:100282","journal-title":"Inform Med Unlocked"},{"key":"216_CR42","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/1320780","author":"D Xie","year":"2017","unstructured":"Xie D, Lei Z, Li B (2017) Deep learning in visual computing and signal processing. Appl Comput Intell Soft Comput. https:\/\/doi.org\/10.1155\/2017\/1320780","journal-title":"Appl Comput Intell Soft Comput"},{"issue":"1","key":"216_CR43","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1186\/s40537-016-0043-6","volume":"3","author":"W Karl","year":"2016","unstructured":"Karl W, Khoshgoftaar TM, Wang DD (2016) A survey of transfer learning. J Big Data 3(1):9","journal-title":"J Big Data"},{"key":"216_CR44","first-page":"3320","volume-title":"Advances in neural information processing systems","author":"J Yosinski","year":"2014","unstructured":"Yosinski J, Clune J, Bengio Y, Lipson H (2014) How transferable are features in deep neural networks? Advances in neural information processing systems. Springer, Singapore, pp 3320\u20133328"},{"key":"216_CR45","first-page":"1097","volume-title":"Advances in neural information processing systems","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems. MIT Press, Cambridge, pp 1097\u20131105"},{"key":"216_CR46","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"216_CR47","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"216_CR48","unstructured":"Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) Squeezenet: alexnet-level accuracy with 50\u00d7 fewer parameters and < 0.5 mb model size. arXiv preprint arXiv:1602.07360"},{"key":"216_CR49","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"216_CR50","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Weinberger KQ (2016) Densely connected convolutional networks. CoRR, abs\/1608.06993, arXiv:1608.06993","DOI":"10.1109\/CVPR.2017.243"},{"key":"216_CR51","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"216_CR52","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi AA (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: AAAI, vol 4, p 12","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"216_CR53","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/j.patcog.2016.11.015","volume":"64","author":"Y Duan","year":"2017","unstructured":"Duan Y, Fang L, Licheng J, Peng Z, Zhang L (2017) SAR image segmentation based on convolutional-wavelet neural network and markov random field. Pattern Recognit 64:255\u2013267","journal-title":"Pattern Recognit"},{"key":"216_CR54","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1016\/j.compeleceng.2017.01.011","volume":"62","author":"P Tang","year":"2017","unstructured":"Tang P, Hanli W (2017) Richer feature for image classification with super and sub kernels based on deep convolutional neural network. Comput Electr Eng 62:499\u2013510","journal-title":"Comput Electr Eng"},{"key":"216_CR55","doi-asserted-by":"crossref","unstructured":"Mendoncya T, Ferreira PM, Marques J, Marcyal ARS, Rozeira J (2013) A dermoscopic image database for research and benchmarking. Presentation in proceedings of PH2 IEEE EMBC","DOI":"10.1109\/EMBC.2013.6610779"},{"key":"216_CR56","unstructured":"Gutman D, Codella NCF, Celebi E, Helba B, Marchetti M, Mishra N, Halpern A (2016) Skin lesion analysis toward melanoma detection: achallenge. In: The international symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC). arXiv preprint arXiv:1605.01397. 2016"},{"key":"216_CR57","unstructured":"Codella NCF, Gutman D, Celebi ME, Helba B, Marchetti MA, Dusza SW, et al (2017) Skin lesion analysis toward melanoma detection: a challenge at the 2017 int. symp. biomed. imaging. arXiv preprint arXiv:1710.05006"},{"issue":"533\u201343","key":"216_CR58","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/S0010-4825(97)00020-6","volume":"27","author":"L Tim","year":"1997","unstructured":"Tim L, Vincent N, Richard G, Andrew C, David M (1997) Dullrazor: a software approach to hair removal from images. Comput Biol Med 27(533\u201343):12. https:\/\/doi.org\/10.1016\/S0010-4825(97)00020-6","journal-title":"Comput Biol Med"},{"issue":"19","key":"216_CR59","doi-asserted-by":"publisher","first-page":"7418","DOI":"10.1016\/j.ijleo.2016.05.027","volume":"127","author":"Q Duan","year":"2016","unstructured":"Duan Q, Akram T, Duan P, Wang X (2016) Visual saliency detection using information contents weighting. Optik 127(19):7418\u20137430","journal-title":"Optik"},{"key":"216_CR60","doi-asserted-by":"publisher","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":"216_CR61","doi-asserted-by":"publisher","first-page":"1476","DOI":"10.1016\/j.procs.2015.02.067","volume":"46","author":"AS Sankar","year":"2015","unstructured":"Sankar AS, Nair SS, Dharan VS, Sankaran P (2015) Wavelet sub band entropy based feature extraction method for BCI. Procedia Comput Sci 46:1476\u20131482","journal-title":"Procedia Comput Sci"},{"key":"216_CR62","first-page":"513","volume-title":"Advances in neural information processing systems","author":"J Goldberger","year":"2005","unstructured":"Goldberger J, Hinton GE, Roweis ST, Salakhutdinov RR (2005) Neighbourhood components analysis. Advances in neural information processing systems. MIT Press, Cambridge, pp 513\u2013520"},{"issue":"1","key":"216_CR63","first-page":"161","volume":"7","author":"Y Wei","year":"2012","unstructured":"Wei Y, Kuanquan W, Wangmeng Z (2012) Neighborhood component feature selection for high-dimensional data. JCP 7(1):161\u2013168","journal-title":"JCP"},{"key":"216_CR64","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.patcog.2018.08.001","volume":"85","author":"L Bi","year":"2019","unstructured":"Bi L, Kim J, Ahn E, Kumar A, Feng D, Fulham M (2019) Step-wise integration of deep class-specific learning for dermoscopic image segmentation. Pattern Recognit 85:78\u201389","journal-title":"Pattern Recognit"},{"issue":"9","key":"216_CR65","first-page":"189","volume":"9","author":"I Zaqout","year":"2016","unstructured":"Zaqout I (2016) Diagnosis of skin lesions based on dermoscopic images using image processing techniques. Int J Signal Process Image Process Pattern Recognit 9(9):189\u2013204","journal-title":"Int J Signal Process Image Process Pattern Recognit"},{"issue":"1","key":"216_CR66","doi-asserted-by":"publisher","first-page":"1603","DOI":"10.1186\/s40064-016-3211-4","volume":"5","author":"K Shehzad","year":"2016","unstructured":"Shehzad K, Uzma J, Kashif S, Usman Akram M, Manzoor W, Ahmed W, Sohail A (2016) Segmentation of skin lesion using Cohen-Daubechies-Feauveau biorthogonal wavelet. SpringerPlus 5(1):1603","journal-title":"SpringerPlus"},{"key":"216_CR67","doi-asserted-by":"crossref","unstructured":"Waheed Z, Waheed A, Zafar M, Raiz F (2017) An efficient machine learning approach for the detection of melanoma using dermoscopic images. In: International conference on communication, computing and digital systems (C-CODE). IEEE, New York, pp 316\u2013319","DOI":"10.1109\/C-CODE.2017.7918949"},{"issue":"8","key":"216_CR68","doi-asserted-by":"publisher","first-page":"1096","DOI":"10.1049\/iet-cvi.2018.5238","volume":"12","author":"NN Sultana","year":"2018","unstructured":"Sultana NN, Mandal B, Puhan NB (2018) Deep residual network with regularised fisher framework for detection of melanoma. IET Comput Vis 12(8):1096\u20131104","journal-title":"IET Comput Vis"},{"key":"216_CR69","doi-asserted-by":"crossref","unstructured":"Harangi B, Baran A, Hajdu A (2018) Classification of skin lesions using an ensemble of deep neural networks. In: 2018 40th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 2575\u20132578","DOI":"10.1109\/EMBC.2018.8512800"}],"container-title":["Human-centric Computing and Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13673-020-00216-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13673-020-00216-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13673-020-00216-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T02:10:43Z","timestamp":1666231843000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s13673-020-00216-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,31]]},"references-count":69,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["216"],"URL":"https:\/\/doi.org\/10.1186\/s13673-020-00216-y","relation":{},"ISSN":["2192-1962"],"issn-type":[{"value":"2192-1962","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,31]]},"assertion":[{"value":"15 April 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 March 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 March 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"12"}}