{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T23:24:46Z","timestamp":1780356286799,"version":"3.54.1"},"reference-count":110,"publisher":"Springer Science and Business Media LLC","issue":"32","license":[{"start":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T00:00:00Z","timestamp":1708905600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T00:00:00Z","timestamp":1708905600000},"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\/s11042-024-18119-w","type":"journal-article","created":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T02:02:03Z","timestamp":1708912923000},"page":"78093-78124","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["A systematic literature survey on skin disease detection and classification using machine learning and deep learning"],"prefix":"10.1007","volume":"83","author":[{"given":"Rashmi","family":"Yadav","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5475-8664","authenticated-orcid":false,"given":"Aruna","family":"Bhat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,26]]},"reference":[{"key":"18119_CR1","unstructured":"ADDI Project (2013) https:\/\/www.fc.up.pt\/addi\/ph2%20database.html. PH2 Dataset, Accessed January 30, 2023"},{"key":"18119_CR2","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1007\/s10462-020-09865-y","volume":"54","author":"A Adegun","year":"2021","unstructured":"Adegun A, Viriri S (2021) Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-art. Artif Intell Rev 54:811\u2013841","journal-title":"Artif Intell Rev"},{"key":"18119_CR3","doi-asserted-by":"publisher","unstructured":"Devakishan Adla et al. (2022) Deep learning-based computer aided diagnosis model for skin cancer detection and classification. In: Distributed and Parallel Databases 40.4, pp. 717\u2013736. https:\/\/doi.org\/10.1007\/s10619-021-07360-z","DOI":"10.1007\/s10619-021-07360-z"},{"issue":"10","key":"18119_CR4","first-page":"10109","volume":"34","author":"Md Sadia Afroze","year":"2022","unstructured":"Sadia Afroze Md, Hossain Rajib, Hoque Mohammed Moshiul (2022) Deepfocus: A visual focus of attention detection framework using deep learning in multi-object scenarios. In J King Saud Univ-Comput Inf Sci 34(10):10109\u201310124","journal-title":"In J King Saud Univ-Comput Inf Sci"},{"key":"18119_CR5","doi-asserted-by":"publisher","unstructured":"Afroze S et al. (2023) An empirical framework for detecting speaking modes using ensemble classifier. In: Multimed Tools Appl, pp. 1\u201334. https:\/\/doi.org\/10.1007\/s11042-023-15254-8","DOI":"10.1007\/s11042-023-15254-8"},{"key":"18119_CR6","doi-asserted-by":"crossref","first-page":"119064","DOI":"10.1016\/j.eswa.2022.119064","volume":"213","author":"Fayadh Alenezi","year":"2023","unstructured":"Alenezi Fayadh, Armghan Ammar, Polat Kemal (2023) Wavelet transform based deep residual neural network and ReLU based Extreme Learning Machine for skin lesion classification. In Exp Syst Appl 213:119064","journal-title":"In Exp Syst Appl"},{"key":"18119_CR7","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.procs.2019.12.090","volume":"163","author":"Nawal Soliman ALKolifi ALEnezi","year":"2019","unstructured":"Nawal Soliman ALKolifi ALEnezi (2019) A method of skin disease detection using image processing and machine learning. Proced Comput Sci 163:85\u201392","journal-title":"Proced Comput Sci"},{"key":"18119_CR8","first-page":"4009","volume":"11.23","author":"Saleh Naif Almuayqil","year":"2022","unstructured":"Almuayqil Saleh Naif, El-Ghany Sameh Abd, Elmogy Mohammed (2022) Computer-Aided Di- agnosis for Early Signs of Skin Diseases Using Multi Types Feature Fusion Based on a Hybrid Deep Learning Model. In: Electron 11.23:4009","journal-title":"In: Electron"},{"issue":"2","key":"18119_CR9","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.3390\/healthcare10122481","volume":"10","author":"G Alwakid","year":"2022","unstructured":"Alwakid G, Gouda W, Humayun M, Sama NU (2022) Melanoma detection using deep learning-based classifications. Healthcare 10(2):2481","journal-title":"Healthcare"},{"key":"18119_CR10","doi-asserted-by":"crossref","first-page":"37379","DOI":"10.1007\/s11042-021-11628-y","volume":"8126","author":"Vatsala Anand","year":"2022","unstructured":"Anand Vatsala et al (2022) An automated deep learning models for classification of skin disease using Dermoscopy images: A comprehensive study. Multimed Tools Appl 8126:37379\u201337401","journal-title":"Multimed Tools Appl"},{"key":"18119_CR11","first-page":"119230","volume":"213","author":"Vatsala Anand","year":"2023","unstructured":"Anand Vatsala et al (2023) Fusion of U-Net and CNN model for segmentation and classification of skin lesion from dermoscopy images. In: Exp Syst Appl 213:119230","journal-title":"In: Exp Syst Appl"},{"key":"18119_CR12","unstructured":"Argenziano G, Soyer HP, De Giorgi V, Piccolo D, Carli P, Delfino M (2000) Interactive atlas of dermoscopy (book and CD-ROM)"},{"key":"18119_CR13","first-page":"815","volume":"351","author":"Hossam Magdy Balaha and Asmaa El-Sayed Hassan","year":"2023","unstructured":"Hossam Magdy Balaha and Asmaa El-Sayed Hassan (2023) Skin cancer diagnosis based on deep transfer learning and sparrow search algorithm. Neural Comput Appl 351:815\u2013853","journal-title":"Neural Comput Appl"},{"key":"18119_CR14","doi-asserted-by":"publisher","unstructured":"Bandy AD et al. (2023) Intraclass clustering-based CNN approach for detection of malignant melanoma. In: Sensors 23.2, p. 926. https:\/\/doi.org\/10.3390\/s23020926","DOI":"10.3390\/s23020926"},{"key":"18119_CR15","first-page":"1096","volume":"233","author":"M Catarina Barata","year":"2018","unstructured":"Catarina Barata M, Celebi Emre, Marques Jorge S (2018) A survey of feature extraction in der- moscopy image analysis of skin cancer. IEEE J Biomed Health Inf 233:1096\u20131109","journal-title":"IEEE J Biomed Health Inf"},{"key":"18119_CR16","doi-asserted-by":"crossref","unstructured":"Bissoto A, Perez F, Valle E, Avila S (2018) Skin lesion synthesis with generative adversarial networks. In:\u00a0OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy,\u00a0Clinical Image-Based Procedures, and Skin Image Analysis: First International Workshop, OR 2.0 2018, 5th\u00a0International Workshop, CARE 2018, 7th International Workshop, CLIP 2018, Third International Workshop, ISIC\u00a02018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16 and 20, 2018, Proceedings 5. Springer International Publishing, pp 294\u2013302","DOI":"10.1007\/978-3-030-01201-4_32"},{"key":"18119_CR17","first-page":"9960","volume":"12.19","author":"Marta Bistron\u00b4 and Zbigniew Piotrow","year":"2022","unstructured":"Marta Bistron\u00b4 and Zbigniew Piotrowski (2022) Comparison of Machine Learning Algorithms Used for Skin Cancer Diagnosis. In: Appl Sci 12.19:9960","journal-title":"In: Appl Sci"},{"key":"18119_CR18","first-page":"144","volume-title":"A training algorithm for optimal margin classifiers","author":"BE Boser","year":"1992","unstructured":"Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Proceedings of the fifth annual workshop on Computational learning theory, pp 144\u2013152"},{"key":"18119_CR19","first-page":"123","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman L (1996) Bagging predictors. Mach Learn 24:123\u2013140","journal-title":"Mach Learn"},{"key":"18119_CR20","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45:5\u201332","journal-title":"Mach Learn"},{"key":"18119_CR21","first-page":"104659","volume":"230","author":"Priya Choudhary","year":"2022","unstructured":"Choudhary Priya, Singhai Jyoti, Yadav JS (2022) Skin lesion detection based on deep neural networks. In: Chemometrics Intell Lab Syst 230:104659","journal-title":"In: Chemometrics Intell Lab Syst"},{"key":"18119_CR22","unstructured":"Mayo Clinic (2022) Actinic Keratosis. https:\/\/www.mayoclinic.org\/diseases.conditions\/actinic-keratosis\/symptoms-causes\/syc-20354969. Accessed May 15, 2023. December 17"},{"key":"18119_CR23","unstructured":"Mayo Clinic (2023) Atopic dermatitis (eczema). https:\/\/www.mayoclinic.org\/diseases-conditions\/atopic-dermatitis-eczema\/symptoms-causes\/syc-20353273. Accessed May 15, 2023. May 09"},{"key":"18119_CR24","unstructured":"Mayo Clinic (2021) Basal Cell Carcinoma. https:\/\/www.mayoclinic.org\/diseases-conditions\/.basal-cell-carcinoma\/symptoms-causes\/syc-20354187. Accessed May 15, 2023. October 01"},{"key":"18119_CR25","unstructured":"Mayo Clinic (2022) Melanoma. https:\/\/www.mayoclinic.org\/diseases-conditions\/melanoma\/symptoms-causes\/syc-20374884.Melanoma, Accessed May 15, 2023. June 18"},{"key":"18119_CR26","unstructured":"Mayo Clinic (2022) Psoriasis. https:\/\/www.mayoclinic.org\/diseases-conditions\/psoriasis\/symptoms-causes\/syc-20355840. Accessed May 15, 2023. October 08"},{"key":"18119_CR27","unstructured":"Mayo Clinic (2021) Rosacea. https:\/\/www.mayoclinic.org\/diseases.conditions\/rosacea\/symptoms-causes\/syc-20353815. Accessed May 15, 2023. September 22"},{"key":"18119_CR28","unstructured":"Mayo Clinic (2022) Seborrheic Keratosis. https:\/\/www.mayoclinic.org\/diseases-conditions\/seborrheic-keratosis\/symptoms-causes\/syc-20353878. Accessed May 15, 2023. January 18"},{"key":"18119_CR29","unstructured":"Mayo Clinic (2022) Seborrheic keratosis. https:\/\/www.mayoclinic.org\/diseases-conditions\/seborrheic-keratosis\/symptoms-causes\/syc-20353878. Seborrheic Keratosis, Accessed May 15, 2023. January 18"},{"key":"18119_CR30","unstructured":"Mayo Clinic (2021) Squamous Cell Carcinoma of the skin. https:\/\/www.mayoclinic.org\/diseases.conditions\/squamous-cell-carcinoma\/symptoms-causes\/syc-20352480. Accessed May 15, 2023. May 13"},{"key":"18119_CR31","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/MSP.2017.2765202","volume":"351","author":"Antonia Creswell","year":"2018","unstructured":"Creswell Antonia et al (2018) Generative adversarial networks: An overview. In IEEE Signal process Mag 351:53\u201365","journal-title":"In IEEE Signal process Mag"},{"key":"18119_CR32","first-page":"1","volume-title":"2020 5th international conference on advanced technologies for signal and image processing (ATSIP)","author":"J Daghrir","year":"2020","unstructured":"Daghrir J, Tlig L, Bouchouicha M, Sayadi M (2020) Melanoma skin cancer detection using deep learning and classical machine learning techniques: a hybrid approach. In: 2020 5th international conference on advanced technologies for signal and image processing (ATSIP). IEEE, pp 1\u20135"},{"key":"18119_CR33","unstructured":"Dermatology Information System (2023) https:\/\/www.dermis.net\/dermisroot\/en\/home\/index.htm\/.DermIS.Dataset, Accessed January 30"},{"key":"18119_CR34","unstructured":"DermNet-All about Skin (1998) https:\/\/dermnetnz.org\/.DermNet.Dataset, Accessed January 30, 2023"},{"key":"18119_CR35","unstructured":"DERMOFIT Project Datase (2023) https:\/\/homepages.inf.ed.ac.uk\/rbf\/DERMOFIT\/datasets.htm.DermoFit Dataset, Accessed January 30"},{"key":"18119_CR36","first-page":"5479","volume":"1810","author":"Mehwish Dildar","year":"2021","unstructured":"Dildar Mehwish et al (2021) Skin cancer detection: a review using deep learning techniques. In: Int J Environ Res Publ Health 1810:5479","journal-title":"In: Int J Environ Res Publ Health"},{"key":"18119_CR37","first-page":"2369","volume":"822","author":"Tausif Diwan","year":"2023","unstructured":"Diwan Tausif et al (2023) Model hybridization & learning rate annealing for skin cancer detection. In: Multimed Tools Appl 822:2369\u20132392","journal-title":"In: Multimed Tools Appl"},{"key":"18119_CR38","unstructured":"Fanconi C (2018) Skin Cancer: Malignant Vs Benign. https:\/wwwkaggle.com\/ datasets\/fanconic\/skin-cancer-malignant-vs-benign.Skin Cancer, Accessed January 30, 2023"},{"key":"18119_CR39","doi-asserted-by":"publisher","unstructured":"Farag A et al. (2016) A bottom-up approach for pancreas segmentation using cascaded superpixels and (deep) image patch labeling. In: IEEE Trans Image Process 26.1, pp. 386\u2013399. https:\/\/doi.org\/10.1109\/TIP.2016.2624198","DOI":"10.1109\/TIP.2016.2624198"},{"key":"18119_CR40","first-page":"104186","volume":"79","author":"Himanshu K Gajera","year":"2023","unstructured":"Gajera Himanshu K, Nayak Deepak Ranjan, Zaveri Mukesh A (2023) A comprehensive analysis of dermoscopy images for melanoma detection via deep CNN features. In: Biomed Signal Process Control 79:104186","journal-title":"In: Biomed Signal Process Control"},{"key":"18119_CR41","first-page":"5138","volume":"18.8","author":"Biswarup Ganguly","year":"2021","unstructured":"Ganguly Biswarup, Dey Debangshu, Munshi Sugata (2021) Image visibility filter-based inter- pretable deep learning framework for skin lesion diagnosis. IEEE Trans Industr Inf 18.8:5138\u20135147","journal-title":"IEEE Trans Industr Inf"},{"key":"18119_CR42","doi-asserted-by":"publisher","unstructured":"Ghahfarrokhi SS et al. (2023) Malignant melanoma diagnosis applying a machine learning method based on the combination of nonlinear and texture features. In: Biomedical Signal Processing and Control 80, p. 104300. https:\/\/doi.org\/10.1016\/j.bspc.2022.104300","DOI":"10.1016\/j.bspc.2022.104300"},{"key":"18119_CR43","doi-asserted-by":"publisher","unstructured":"Goceri E (2021) Deep learning-based classification of facial dermatological disorders. In: Comput Biol Med 128, p. 104118. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.104118","DOI":"10.1016\/j.compbiomed.2020.104118"},{"key":"18119_CR44","doi-asserted-by":"publisher","unstructured":"Goceri E (2021) Diagnosis of skin diseases in the era of deep learning and mobile technology. In: Computers in Biology and Medicine 134, p. 104458. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104458","DOI":"10.1016\/j.compbiomed.2021.104458"},{"key":"18119_CR45","unstructured":"Gonzalez RC, Woods RE (2008) Digital Image Processing. Prentice Hall. isbn: 9780131687288. url: https:\/\/books.google.co.in\/books?id=8uGOnjRGEzoC. Accessed\u00a012 May 2023"},{"key":"18119_CR46","doi-asserted-by":"publisher","unstructured":"Goodfellow I et al. (2020) Generative adversarial networks. In: Communications of the ACM 63.11, pp. 139\u2013144. https:\/\/doi.org\/10.1145\/3422622","DOI":"10.1145\/3422622"},{"key":"18119_CR47","doi-asserted-by":"publisher","unstructured":"Hatem MQ (2022) Skin lesion classification system using a K-nearest neighbor algorithm. In: Visual Computing for Industry, Biomedicine, and Art 5.1, pp. 1\u201310. https:\/\/doi.org\/10.1186\/s42492-022-00103-6","DOI":"10.1186\/s42492-022-00103-6"},{"key":"18119_CR48","first-page":"770","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"K He","year":"2016","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"},{"key":"18119_CR49","unstructured":"Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531"},{"key":"18119_CR50","doi-asserted-by":"publisher","unstructured":"Hong Y et al. (2023) Weakly supervised semantic segmentation for skin cancer via CNN super- pixel region response. In: Multimedia Tools and Applications 82.5, pp. 6829\u20136847. https:\/\/doi.org\/10.1007\/s11042-022-13606-4","DOI":"10.1007\/s11042-022-13606-4"},{"key":"18119_CR51","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"18119_CR52","doi-asserted-by":"publisher","unstructured":"Hum YC et al. (2022) The development of skin lesion detection application in smart hand- held devices using deep neural networks. In: Multimedia Tools and Applications 81.29, pp. 41579\u201341610. https:\/\/doi.org\/10.1007\/s11042-021-11013-9","DOI":"10.1007\/s11042-021-11013-9"},{"key":"18119_CR53","unstructured":"Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv preprint arXiv:1602.07360"},{"key":"18119_CR54","doi-asserted-by":"crossref","unstructured":"Syed Inthiyaz et al. \u201cSkin disease detection using deep learning\u201d. In: Advances in Engineering Software 175 (2023), p. 103361.","DOI":"10.1016\/j.advengsoft.2022.103361"},{"key":"18119_CR55","first-page":"101843","volume":"88","author":"Imran Iqbal","year":"2021","unstructured":"Iqbal Imran et al (2021) Automated multi-class classification of skin lesions through deep convolu- tional neural network with dermoscopic images. In: Computer Med Imaging Graph 88:101843","journal-title":"In: Computer Med Imaging Graph"},{"key":"18119_CR56","unstructured":"ISIC Challenge (2016) https:\/\/challenge.isic-archive.com\/data\/#2016. ISIC 2016 Dataset, Accessed January 30, 2023"},{"key":"18119_CR57","unstructured":"ISIC Challenge (2017) https:\/\/challenge.isic-archive.com\/data\/#2017. ISIC 2017 Dataset, Accessed January 30, 2023"},{"key":"18119_CR58","unstructured":"ISIC Challenge (2018) https:\/\/challenge.isic-archive.com\/data\/#2018. ISIC 2018 Dataset, Accessed January 30, 2023"},{"key":"18119_CR59","unstructured":"ISIC Challenge (2019) https:\/\/challenge.isic-archive.com\/data\/#2019. ISIC 2019 Dataset, Accessed January 30, 2023"},{"key":"18119_CR60","unstructured":"ISIC Challenge (2020) https:\/\/challenge.isic-archive.com\/data\/#2020. ISIC 2020 Dataset, Accessed January 30, 2023"},{"key":"18119_CR61","first-page":"3713","volume":"79","author":"J Bethanney Janney","year":"2020","unstructured":"Bethanney Janney J, Emalda Roslin S (2020) Classification of melanoma from Dermoscopic data using machine learning techniques. In: Multimed Tools Appl 79:3713\u20133728","journal-title":"In: Multimed Tools Appl"},{"key":"18119_CR62","first-page":"5043","volume":"54.6","author":"SP Karuppiah","year":"2022","unstructured":"Karuppiah SP et al (2022) An Efficient Galactic Swarm Optimization Based Fractal Neural Network Model with DWT for Malignant Melanoma Prediction. In: Neural Process Lett 54.6:5043\u20135062","journal-title":"In: Neural Process Lett"},{"key":"18119_CR63","first-page":"1390","volume":"11.8","author":"Mohamed A Kassem","year":"2021","unstructured":"Kassem Mohamed A et al (2021) Machine learning and deep learning methods for skin lesion classi- fication and diagnosis: a systematic review. In: Diagnostics 11.8:1390","journal-title":"In: Diagnostics"},{"key":"18119_CR64","doi-asserted-by":"publisher","unstructured":"Khan MA et al. (2021) \u201cAttributes based skin lesion detection and recognition: A mask RCNN and transfer learning-based deep learning framework\u201d. In: Pattern Recognition Letters 143, pp. 58\u201366 https:\/\/doi.org\/10.1016\/j.patrec.2020.12.015","DOI":"10.1016\/j.patrec.2020.12.015"},{"key":"18119_CR65","doi-asserted-by":"publisher","unstructured":"Khan MA et al. (2021) Pixels to classes: intelligent learning framework for multiclass skin lesion localization and classification. In: Comput Electric Eng 90, p. 106956. https:\/\/doi.org\/10.1016\/j.compeleceng.2020.106956","DOI":"10.1016\/j.compeleceng.2020.106956"},{"key":"18119_CR66","first-page":"84","volume":"60.6","author":"Alex Krizhevsky","year":"2017","unstructured":"Krizhevsky Alex, Sutskever Ilya, Hinton Geoffrey E (2017) Imagenet classification with deep convolutional neural networks. In: Commun ACM 60.6:84\u201390","journal-title":"In: Commun ACM"},{"key":"18119_CR67","first-page":"108359","volume":"103","author":"K Anup Kumar","year":"2022","unstructured":"Anup Kumar K, Vanmathi C (2022) Optimization driven model and segmentation network for skin cancer detection. In: Comput Electric Eng 103:108359","journal-title":"In: Comput Electric Eng"},{"key":"18119_CR68","doi-asserted-by":"publisher","unstructured":"LeCun Y et al. (1998) Gradient-based learning applied to document recognition. In: Proceedings of the IEEE 86.11, pp. 2278\u20132324. https:\/\/doi.org\/10.1109\/5.726791","DOI":"10.1109\/5.726791"},{"key":"18119_CR69","first-page":"208264","volume":"8","author":"Ling-Fang Li","year":"2020","unstructured":"Li Ling-Fang et al (2020) Deep learning in skin disease image recognition: A review. In: IEEE Access 8:208264\u2013208280","journal-title":"In: IEEE Access"},{"key":"18119_CR70","unstructured":"Mader KS (2018) Skin Cancer MNIST: HAM10000. https:\/\/www.kaggle.com\/datasets\/kmader\/skin-cancer-mnist-ham10000. Skin Cancer, Accessed January 30, 2023"},{"key":"18119_CR71","doi-asserted-by":"publisher","unstructured":"Malibari AA et al. (2022) Optimal deep neural network-driven computer aided diagnosis model for skin cancer. In: Comput Electric Eng 103, p. 108318. https:\/\/doi.org\/10.1016\/j.compeleceng.2022.108318","DOI":"10.1016\/j.compeleceng.2022.108318"},{"key":"18119_CR72","doi-asserted-by":"publisher","unstructured":"Manimurugan S (2023) Hybrid high performance intelligent computing approach of CACNN and RNN for skin cancer image grading. In: Soft Computing 27.1, pp. 579\u2013589. https:\/\/doi.org\/10.1007\/s00500-022-06989-x","DOI":"10.1007\/s00500-022-06989-x"},{"key":"18119_CR73","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.neunet.2023.01.022","volume":"160","author":"S Maqsood","year":"2023","unstructured":"Maqsood S, Dama\u02c7sevi\u02c7cius R (2023) Multiclass skin lesion localization and classifi- cation using deep learnin based features fusion and selection framework for smart healthcare. Neural Netw 160:238\u2013258","journal-title":"Neural Netw"},{"key":"18119_CR74","first-page":"10527","volume":"26.19","author":"K Meena","year":"2022","unstructured":"Meena K et al (2022) A novel method for prediction of skin disease through supervised classification techniques. In: Soft Comput 26.19:10527\u201310533","journal-title":"In: Soft Comput"},{"key":"18119_CR75","first-page":"1210","volume":"13.2","author":"Sufiyan Bashir Mukadam and Hemprasad Yashwant Patil","year":"2023","unstructured":"Sufiyan Bashir Mukadam and Hemprasad Yashwant Patil (2023) Skin Cancer Classification Frame- work Using Enhanced Super Resolution Generative Adversarial Network and Custom Convo- lutional Neural Network. In: Appl Sci 13.2:1210","journal-title":"In: Appl Sci"},{"key":"18119_CR76","first-page":"103727","volume":"81","author":"A Murugan","year":"2021","unstructured":"Murugan A et al (2021) Diagnosis of skin cancer using machine learning techniques. In: Microprocessors Microsyst 81:103727","journal-title":"In: Microprocessors Microsyst"},{"key":"18119_CR77","first-page":"5652","volume":"22.15","author":"Ahmad Naeem","year":"2022","unstructured":"Naeem Ahmad et al (2022) SCDNet: A Deep Learning-Based Framework for the Multiclassification of Skin Cancer Using Dermoscopy Images. In: Sensors 22.15:5652","journal-title":"In: Sensors"},{"key":"18119_CR78","first-page":"103997","volume":"78","author":"Katsuhiro Nakai","year":"2022","unstructured":"Nakai Katsuhiro, Chen Yen-Wei, Han Xian-Hua (2022) Enhanced deep bottleneck transformer model for skin lesion classification. In: Biomed Signal Process Control 78:103997","journal-title":"In: Biomed Signal Process Control"},{"key":"18119_CR79","first-page":"7530","volume":"2219","author":"Viet Dung Nguyen","year":"2022","unstructured":"Nguyen Viet Dung, Bui Ngoc Dung, Do Hoang Khoi (2022) Skin Lesion Classification on Im- balanced Data Using Deep Learning with Soft Attention. In: Sensors 2219:7530","journal-title":"In: Sensors"},{"key":"18119_CR80","unstructured":"Oakley A (2020) Dermatofibroma. https:\/\/dermnetnz.org\/topics\/dermatofibroma. Accessed May 15, 2023. September"},{"key":"18119_CR81","unstructured":"Oakley A (2016) Melanocytic naevus. https:\/\/dermnetnz.org\/topics\/melanocytic.naevus. Accessed May 15, 2023. January"},{"key":"18119_CR82","unstructured":"Oakley A (2016) Vascular proliferations and abnormalities of blood vessels. https:\/\/dermnetnz.org\/topics\/vascular-proliferations-and-abnormalities-of-blood-vessels. Accessed May 15, 2023. February"},{"key":"18119_CR83","unstructured":"Pacheco AGC, Krohling RA (2019) Recent advances in deep learning applied to skin cancer detection. arXiv preprint arXiv:1912.03280"},{"key":"18119_CR84","first-page":"970","volume":"13.2","author":"Seungman Park","year":"2023","unstructured":"Park Seungman et al (2023) FACES: A Deep-Learning-Based Parametric Model to Improve Rosacea Diagnoses. In: Appl Sci 13.2:970","journal-title":"In: Appl Sci"},{"key":"18119_CR85","first-page":"5872","volume":"14.23","author":"Vinayakumar Ravi","year":"2022","unstructured":"Ravi Vinayakumar (2022) Attention Cost-Sensitive Deep Learning-Based Approach for Skin Cancer Detection and Classification. In: Cancers 14.23:5872","journal-title":"In: Cancers"},{"key":"18119_CR86","first-page":"36031","volume":"81.25","author":"Nurullah S\u00b8ahin, Nuh Alpaslan, and","year":"2022","unstructured":"Nurullah S\u00b8ahin, Nuh Alpaslan, and Davut Hanbay (2022) Robust optimization of SegNet hyperpa- rameters for skin lesion segmentation. In: Multimed Tools Appl 81.25:36031\u201336051","journal-title":"In: Multimed Tools Appl"},{"key":"18119_CR87","first-page":"32643","volume":"81.22","author":"Wessam Salma","year":"2022","unstructured":"Salma Wessam, Eltrass Ahmed S (2022) Automated deep learning approach for classification of malignant melanoma and benign skin lesions. In: Multimed Tools Appl 81.22:32643\u201332660","journal-title":"In: Multimed Tools Appl"},{"key":"18119_CR88","first-page":"197","volume":"5","author":"Robert E Schapire","year":"1990","unstructured":"Schapire Robert E (1990) The strength of weak learnability. In: Mach Learn 5:197\u2013227","journal-title":"In: Mach Learn"},{"key":"18119_CR89","first-page":"12039","volume":"33.18","author":"Onur Sevli","year":"2021","unstructured":"Sevli Onur (2021) A deep convolutional neural network-based pigmented skin lesion classification application and experts evaluation. In: Neural Comput Appl 33.18:12039\u201312050","journal-title":"In: Neural Comput Appl"},{"key":"18119_CR90","first-page":"2173","volume":"60.8","author":"Pufang Shan","year":"2022","unstructured":"Shan Pufang et al (2022) Automatic skin lesion classification using a new densely connected convo- lutional network with an SF module. In: Med Biol Eng Comput 60.8:2173\u20132188","journal-title":"In: Med Biol Eng Comput"},{"key":"18119_CR91","first-page":"3155","volume":"82.2","author":"Misaj Sharafudeen","year":"2023","unstructured":"Sharafudeen Misaj (2023) Detecting skin lesions fusing handcrafted features in image network en- sembles. In: Multimed Tools Appl 82.2:3155\u20133175","journal-title":"In: Multimed Tools Appl"},{"key":"18119_CR92","doi-asserted-by":"publisher","unstructured":"Shen S et al. (2022) A low-cost high-performance data augmentation for deep learning-based skin lesion classification. In: BME Frontiers 2022. https:\/\/doi.org\/10.34133\/2022\/9765307","DOI":"10.34133\/2022\/9765307"},{"key":"18119_CR93","doi-asserted-by":"publisher","unstructured":"Shinde RK et al. (2022) Squeeze-MNet: Precise Skin Cancer Detection Model for Low Com- puting IoT Devices Using Transfer Learning In: Cancers 151, p. 12. https:\/\/doi.org\/10.3390\/cancers15010012","DOI":"10.3390\/cancers15010012"},{"key":"18119_CR94","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"18119_CR95","first-page":"103884","volume":"110","author":"Nana Sun","year":"2022","unstructured":"Sun Nana et al (2022) Novel neural network model for predicting susceptibility of facial post- inflammatory hyperpigmentation. In: Med Eng Phys 110:103884","journal-title":"In: Med Eng Phys"},{"key":"18119_CR96","first-page":"2818","volume-title":"Rethinking the inception architecture for computer vision","author":"C Szegedy","year":"2016","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"},{"key":"18119_CR97","first-page":"6105","volume-title":"International conference on machine learning","author":"M Tan","year":"2019","unstructured":"Tan M, Le Q (2019) Efficientnet: rethinking model scaling for convolutional neural networks. In: International conference on machine learning. PMLR, pp 6105\u20136114"},{"key":"18119_CR98","doi-asserted-by":"publisher","unstructured":"Thapar P et al. (2022) A novel hybrid deep learning approach for skin lesion segmentation and classification. In: J Healthcare Eng 2022. https:\/\/doi.org\/10.1155\/2022\/1709842","DOI":"10.1155\/2022\/1709842"},{"key":"18119_CR99","doi-asserted-by":"crossref","unstructured":"Topiwala A, Al-Zogbi L, Fleiter T, Krieger A (2019) Adaptation and evaluation of deep learning techniques for skin segmentation on novel abdominal dataset. In:\u00a02019 IEEE 19th International\u00a0Conference on Bioinformatics and Bioengineering (BIBE). IEEE, pp 752\u2013759","DOI":"10.1109\/BIBE.2019.00141"},{"key":"18119_CR100","first-page":"8311","volume":"22.21","author":"M Muhammad Usama","year":"2022","unstructured":"Muhammad Usama M, Naeem Asif, Mirza Farhaan (2022) Multi-Class Skin Lesions Classification Using Deep Features. In: Sensors 22.21:8311","journal-title":"In: Sensors"},{"key":"18119_CR101","first-page":"341","volume":"190","author":"Anurag Kumar Verma","year":"2020","unstructured":"Verma Anurag Kumar, Pal Saurabh, Kumar Surjeet (2020) Prediction of skin disease using ensemble data mining techniques and feature selection method\u2014a comparative study. In: Appl Biochem Biotechnol 190:341\u2013359","journal-title":"In: Appl Biochem Biotechnol"},{"key":"18119_CR102","volume":"85","author":"L Wang","year":"2023","unstructured":"Wang L, Zhang L, Shu X, Yi Z (2023) Intra-class consistency and inter-class discrimination feature learning for automatic skin lesion classification. Med Image Anal 85:102746","journal-title":"Med Image Anal"},{"issue":"1","key":"18119_CR103","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1186\/s13059-023-03054-0","volume":"24","author":"Y Wang","year":"2023","unstructured":"Wang Y, Wang W, Liu D, Hou W, Zhou T, Ji Z (2023) GeneSegNet: a deep learning framework for cell segmentation by integrating gene expression and imaging. Genome Biol 24(1):235","journal-title":"Genome Biol"},{"key":"18119_CR104","unstructured":"Wang W, Han C, Zhou T, Liu D (2022) Visual recognition with deep nearest centroids. arXiv preprint arXiv:2209.07383"},{"key":"18119_CR105","first-page":"102693","volume":"84","author":"Yongwei Wang","year":"2023","unstructured":"Wang Yongwei et al (2023) Ssd-kd: A self-supervised diverse knowledge distillation method for lightweight skin lesion classification using dermoscopic images. In: Med Image Anal 84:102693","journal-title":"In: Med Image Anal"},{"key":"18119_CR106","first-page":"5596","volume":"26.11","author":"Jeffry Wicaksana","year":"2022","unstructured":"Wicaksana Jeffry et al (2022) Customized Federated Learning for Multi-Source Decentralized Medical Image Classification. In: IEEE J Biomed Health Inf 26.11:5596\u20135607","journal-title":"In: IEEE J Biomed Health Inf"},{"key":"18119_CR107","first-page":"241","volume":"5.2","author":"David H Wolpert","year":"1992","unstructured":"Wolpert David H (1992) Stacked generalization. In: Neural Netw 5.2:241\u2013259","journal-title":"In: Neural Netw"},{"key":"18119_CR108","doi-asserted-by":"crossref","first-page":"66505","DOI":"10.1109\/ACCESS.2019.2918221","volume":"7","author":"ZHE Wu","year":"2019","unstructured":"Wu ZHE et al (2019) Studies on different CNN algorithms for face skin disease classification based on clinical images. IEEE Access 7:66505\u201366511","journal-title":"IEEE Access"},{"key":"18119_CR109","first-page":"105939","volume":"149","author":"Chao Xin","year":"2022","unstructured":"Xin Chao et al (2022) An improved transformer network for skin cancer classification. In: Comput Biol Med 149:105939","journal-title":"In: Comput Biol Med"},{"key":"18119_CR110","doi-asserted-by":"crossref","unstructured":"Yang L, Wang Q, Wang J, Quan X, Feng F, Chen Y,\u00a0Khabsa M, Wang S, Xu Z, Liu D (2023) MixPAVE: mix-prompt tuning for few-shot product attribute value extraction. In: Findings of the Association for Computational Linguistics: ACL 2023, pp 9978\u20139991","DOI":"10.18653\/v1\/2023.findings-acl.633"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18119-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-18119-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18119-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T04:21:59Z","timestamp":1725423719000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-18119-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,26]]},"references-count":110,"journal-issue":{"issue":"32","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["18119"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-18119-w","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,26]]},"assertion":[{"value":"15 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 September 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 January 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 February 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"There are no studies by any of the authors in this article that used humans or animals as subjects.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Author 1 declares that she has no conflict of interest.Author 2 declares that she has no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}