{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T15:05:42Z","timestamp":1768921542313,"version":"3.49.0"},"reference-count":89,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T00:00:00Z","timestamp":1713225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T00:00:00Z","timestamp":1713225600000},"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":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-024-01106-w","type":"journal-article","created":{"date-parts":[[2024,4,16]],"date-time":"2024-04-16T19:01:27Z","timestamp":1713294087000},"page":"2581-2596","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Skin Cancer Image Segmentation Based on Midpoint Analysis Approach"],"prefix":"10.1007","volume":"37","author":[{"given":"Uzma","family":"Saghir","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9658-1441","authenticated-orcid":false,"given":"Shailendra Kumar","family":"Singh","sequence":"additional","affiliation":[]},{"given":"Moin","family":"Hasan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,16]]},"reference":[{"key":"1106_CR1","doi-asserted-by":"publisher","unstructured":"N. H. Khan et al., \u201cSkin cancer biology and barriers to treatment: Recent applications of polymeric micro\/nanostructures,\u201d Journal of Advanced Research, vol. 36. Elsevier B.V., pp. 223\u2013247, Feb. 01, 2022. https:\/\/doi.org\/10.1016\/j.jare.2021.06.014.","DOI":"10.1016\/j.jare.2021.06.014"},{"key":"1106_CR2","doi-asserted-by":"publisher","unstructured":"M. Dildar et al., \u201cSkin cancer detection: A review using deep learning techniques,\u201d International Journal of Environmental Research and Public Health, vol. 18, no. 10. MDPI AG, May 02, 2021. https:\/\/doi.org\/10.3390\/ijerph18105479.","DOI":"10.3390\/ijerph18105479"},{"key":"1106_CR3","unstructured":"R. Nuthan and V. Rohith, \u201cDetection of Skin Cancer Using KNN and Naive Bayes Algorithms R. Nuthan 1 , V. Rohith 2*,\u201d vol. 12, no. 6, pp. 8257\u20138266, 2021."},{"key":"1106_CR4","doi-asserted-by":"publisher","unstructured":"U. Saghir and V. Devendran, \u201cA Brief Review of Feature Extraction Methods for Melanoma Detection,\u201d in 2021 7th International Conference on Advanced Computing and Communication Systems, ICACCS 2021, Institute of Electrical and Electronics Engineers Inc., Mar. 2021, pp. 1304\u20131307. https:\/\/doi.org\/10.1109\/ICACCS51430.2021.9441787.","DOI":"10.1109\/ICACCS51430.2021.9441787"},{"issue":"4","key":"1106_CR5","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1016\/j.det.2017.06.001","volume":"35","author":"AM Glazer","year":"2017","unstructured":"A. M. Glazer, D. S. Rigel, R. R. Winkelmann, and A. S. Farberg, \u201cClinical Diagnosis of Skin Cancer: Enhancing Inspection and Early Recognition,\u201d Dermatol Clin, vol. 35, no. 4, pp. 409\u2013416, 2017. https:\/\/doi.org\/10.1016\/j.det.2017.06.001.","journal-title":"Dermatol Clin"},{"issue":"4","key":"1106_CR6","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1016\/j.ijwd.2021.05.005","volume":"7","author":"A Jiang","year":"2021","unstructured":"A. Jiang et al., \u201cSkin cancer discovery during total body skin examinations,\u201d Int J Womens Dermatol, vol. 7, no. 4, pp. 411\u2013414, Sep. 2021. https:\/\/doi.org\/10.1016\/j.ijwd.2021.05.005.","journal-title":"Int J Womens Dermatol"},{"key":"1106_CR7","doi-asserted-by":"publisher","first-page":"90132","DOI":"10.1109\/ACCESS.2019.2926837","volume":"7","author":"MQ Khan","year":"2019","unstructured":"M. Q. Khan et al., \u201cClassification of Melanoma and Nevus in Digital Images for Diagnosis of Skin Cancer,\u201d IEEE Access, vol. 7, pp. 90132\u201390144, 2019. https:\/\/doi.org\/10.1109\/ACCESS.2019.2926837.","journal-title":"IEEE Access"},{"issue":"4","key":"1106_CR8","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1007\/s40257-020-00517-z","volume":"21","author":"HD Heibel","year":"2020","unstructured":"H. D. Heibel, L. Hooey, and C. J. Cockerell, \u201cA Review of Noninvasive Techniques for Skin Cancer Detection in Dermatology,\u201d Am J Clin Dermatol, vol. 21, no. 4, pp. 513\u2013524, 2020. https:\/\/doi.org\/10.1007\/s40257-020-00517-z.","journal-title":"Am J Clin Dermatol"},{"issue":"August","key":"1106_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fmed.2019.00180","volume":"6","author":"J Kato","year":"2019","unstructured":"J. Kato, K. Horimoto, S. Sato, T. Minowa, and H. Uhara, \u201cDermoscopy of Melanoma and Non-melanoma Skin Cancers,\u201d Front Med (Lausanne), vol. 6, no. August, pp. 1\u20137, 2019. https:\/\/doi.org\/10.3389\/fmed.2019.00180.","journal-title":"Front Med (Lausanne)"},{"issue":"3","key":"1106_CR10","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1111\/j.1365-2133.2008.08713.x","volume":"159","author":"ME Vestergaard","year":"2008","unstructured":"M. E. Vestergaard, P. Macaskill, P. E. Holt, and S. W. Menzies, \u201cDermoscopy compared with naked eye examination for the diagnosis of primary melanoma: A meta-analysis of studies performed in a clinical setting,\u201d British Journal of Dermatology, vol. 159, no. 3, pp. 669\u2013676, 2008. https:\/\/doi.org\/10.1111\/j.1365-2133.2008.08713.x.","journal-title":"British Journal of Dermatology"},{"issue":"1","key":"1106_CR11","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/s0262-8856(98)00091-2","volume":"17","author":"L Xu","year":"1999","unstructured":"L. Xu et al., \u201cSegmentation of skin cancer images,\u201d Image Vis Comput, vol. 17, no. 1, pp. 65\u201374, 1999. https:\/\/doi.org\/10.1016\/s0262-8856(98)00091-2.","journal-title":"Image Vis Comput"},{"issue":"1","key":"1106_CR12","doi-asserted-by":"publisher","first-page":"50","DOI":"10.2174\/1573405614666180911120546","volume":"16","author":"NS Zghal","year":"2018","unstructured":"N. S. Zghal and N. Derbel, \u201cMelanoma Skin Cancer Detection based on Image Processing,\u201d Current Medical Imaging Formerly Current Medical Imaging Reviews, vol. 16, no. 1, pp. 50\u201358, 2018. https:\/\/doi.org\/10.2174\/1573405614666180911120546.","journal-title":"Current Medical Imaging Formerly Current Medical Imaging Reviews"},{"issue":"Iccmit","key":"1106_CR13","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1016\/j.procs.2015.09.027","volume":"65","author":"NM Zaitoun","year":"2015","unstructured":"N. M. Zaitoun and M. J. Aqel, \u201cSurvey on Image Segmentation Techniques,\u201d Procedia Comput Sci, vol. 65, no. Iccmit, pp. 797\u2013806, 2015. https:\/\/doi.org\/10.1016\/j.procs.2015.09.027.","journal-title":"Procedia Comput Sci"},{"key":"1106_CR14","doi-asserted-by":"publisher","unstructured":"M. K. Hasan, M. A. Ahamad, C. H. Yap, and G. Yang (2023) A survey, review, and future trends of skin lesion segmentation and classification. Comput Biol Med 155(January). https:\/\/doi.org\/10.1016\/j.compbiomed.2023.106624.","DOI":"10.1016\/j.compbiomed.2023.106624"},{"issue":"6","key":"1106_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1504\/IJBRA.2022.10053584","volume":"18","author":"MA Siddique","year":"2022","unstructured":"M. A. Siddique and S. K. Singh, \u201cA Survey of Computer Vision based Liver Cancer Detection,\u201d Int J Bioinform Res Appl, vol. 18, no. 6, p. 1, 2022. https:\/\/doi.org\/10.1504\/IJBRA.2022.10053584.","journal-title":"Int J Bioinform Res Appl"},{"key":"1106_CR16","doi-asserted-by":"publisher","unstructured":"D. Divya and T. R. Ganesh Babu, \u201cA Survey on Image Segmentation Techniques,\u201d Lecture Notes on Data Engineering and Communications Technologies, vol. 35, pp. 1107\u20131114, 2020. https:\/\/doi.org\/10.1007\/978-3-030-32150-5_112.","DOI":"10.1007\/978-3-030-32150-5_112"},{"key":"1106_CR17","doi-asserted-by":"crossref","unstructured":"A. T. Beuren, R. Valentim, C. Palavro, R. Janasieivicz, R. A. Folloni, and J. Facon, \u201cSkin Melanoma Segmentation by Morphological Approach,\u201d pp. 972\u2013978, 2012.","DOI":"10.1145\/2345396.2345553"},{"key":"1106_CR18","doi-asserted-by":"publisher","unstructured":"M. A. Ahmed Thaajwer and U. A. Piumi Ishanka, \u201cMelanoma skin cancer detection using image processing and machine learning techniques,\u201d ICAC 2020 - 2nd International Conference on Advancements in Computing, Proceedings, pp. 363\u2013368, 2020. https:\/\/doi.org\/10.1109\/ICAC51239.2020.9357309.","DOI":"10.1109\/ICAC51239.2020.9357309"},{"issue":"6","key":"1106_CR19","first-page":"1","volume":"30","author":"R Javid","year":"2019","unstructured":"R. Javid, M. S. M. Rahim, T. Saba, and M. Rashid, \u201cRegion-based active contour JSEG fusion technique for skin lesion segmentation from dermoscopic images,\u201d Biomedical Research, vol. 30, no. 6, pp. 1\u201310, 2019.","journal-title":"Biomedical Research"},{"issue":"5","key":"1106_CR20","doi-asserted-by":"publisher","first-page":"7397","DOI":"10.1007\/s11042-020-10064-8","volume":"80","author":"S Garg","year":"2021","unstructured":"S. Garg and B. Jindal, \u201cSkin lesion segmentation using k-mean and optimized fire fly algorithm,\u201d Multimed Tools Appl, vol. 80, no. 5, pp. 7397\u20137410, 2021. https:\/\/doi.org\/10.1007\/s11042-020-10064-8.","journal-title":"Multimed Tools Appl"},{"issue":"2","key":"1106_CR21","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1111\/srt.12252","volume":"22","author":"R Kasmi","year":"2016","unstructured":"R. Kasmi, K. Mokrani, R. K. Rader, J. G. Cole, and W. V. Stoecker, \u201cBiologically inspired skin lesion segmentation using a geodesic active contour technique,\u201d Skin Research and Technology, vol. 22, no. 2, pp. 208\u2013222, 2016. https:\/\/doi.org\/10.1111\/srt.12252.","journal-title":"Skin Research and Technology"},{"issue":"2","key":"1106_CR22","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1109\/JBHI.2018.2832455","volume":"23","author":"F Riaz","year":"2019","unstructured":"F. Riaz, S. Naeem, R. Nawaz, and M. Coimbra, \u201cActive Contours Based Segmentation and Lesion Periphery Analysis for Characterization of Skin Lesions in Dermoscopy Images,\u201d IEEE J Biomed Health Inform, vol. 23, no. 2, pp. 489\u2013500, 2019. https:\/\/doi.org\/10.1109\/JBHI.2018.2832455.","journal-title":"IEEE J Biomed Health Inform"},{"key":"1106_CR23","doi-asserted-by":"publisher","unstructured":"A. Bassel, A. B. Abdulkareem, Z. A. A. Alyasseri, N. S. Sani, and H. J. Mohammed, \u201cAutomatic Malignant and Benign Skin Cancer Classification Using a Hybrid Deep Learning Approach,\u201d Diagnostics, vol. 12, no. 10, Oct. 2022. https:\/\/doi.org\/10.3390\/diagnostics12102472.","DOI":"10.3390\/diagnostics12102472"},{"key":"1106_CR24","doi-asserted-by":"publisher","unstructured":"M. Dildar et al., \u201cSkin cancer detection: A review using deep learning techniques,\u201d Int J Environ Res Public Health, vol. 18, no. 10, 2021. https:\/\/doi.org\/10.3390\/ijerph18105479.","DOI":"10.3390\/ijerph18105479"},{"issue":"7639","key":"1106_CR25","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"A. Esteva et al., \u201cDermatologist-level classification of skin cancer with deep neural networks,\u201d Nature, vol. 542, no. 7639, pp. 115\u2013118, 2017. https:\/\/doi.org\/10.1038\/nature21056.","journal-title":"Nature"},{"issue":"3","key":"1106_CR26","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1109\/TMI.2016.2633551","volume":"36","author":"F Xie","year":"2017","unstructured":"F. Xie, H. Fan, Y. Li, Z. Jiang, R. Meng, and A. Bovik, \u201cMelanoma classification on dermoscopy images using a neural network ensemble model,\u201d IEEE Trans Med Imaging, vol. 36, no. 3, pp. 849\u2013858, 2017. https:\/\/doi.org\/10.1109\/TMI.2016.2633551.","journal-title":"IEEE Trans Med Imaging"},{"key":"1106_CR27","doi-asserted-by":"publisher","unstructured":"M. u. Rehman, S. H. Khan, S. M. Danish Rizvi, Z. Abbas and A. Zafar, \u201cClassification of Skin Lesion by Interference of Segmentation and Convolotion Neural Network,\u201d 2018 2nd International Conference on Engineering Innovation (ICEI),\u00a0Bangkok, Thailand,\u00a0pp. 81\u201385, 2018. https:\/\/doi.org\/10.1109\/ICEI18.2018.8448814.","DOI":"10.1109\/ICEI18.2018.8448814"},{"key":"1106_CR28","doi-asserted-by":"publisher","unstructured":"H. Mahmoud, M. Abdel-Nasser and O. A. Omer, \u201cComputer aided diagnosis system for skin lesions detection using texture analysis methods,\u201d\u00a02018 International Conference on Innovative Trends in Computer Engineering (ITCE), Aswan, Egypt, pp. 140\u2013144, 2018. https:\/\/doi.org\/10.1109\/ITCE.2018.8327948.","DOI":"10.1109\/ITCE.2018.8327948"},{"key":"1106_CR29","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.bspc.2017.07.010","volume":"39","author":"S Pathan","year":"2018","unstructured":"S. Pathan, K. G. Prabhu, and P. C. Siddalingaswamy, \u201cTechniques and algorithms for computer aided diagnosis of pigmented skin lesions\u2014A review,\u201d Biomed Signal Process Control, vol. 39, pp. 237\u2013262, 2018. https:\/\/doi.org\/10.1016\/j.bspc.2017.07.010.","journal-title":"Biomed Signal Process Control"},{"key":"1106_CR30","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1016\/j.cmpb.2018.05.027","volume":"162","author":"MA Al-masni","year":"2018","unstructured":"M. A. Al-masni, M. A. Al-antari, M. T. Choi, S. M. Han, and T. S. Kim, \u201cSkin lesion segmentation in dermoscopy images via deep full resolution convolutional networks,\u201d Comput Methods Programs Biomed, vol. 162, pp. 221\u2013231, 2018. https:\/\/doi.org\/10.1016\/j.cmpb.2018.05.027.","journal-title":"Comput Methods Programs Biomed"},{"key":"1106_CR31","doi-asserted-by":"publisher","unstructured":"E. V. C. B and D. Ron-dom, Technology Trends, vol. 895. in Communications in Computer and Information Science, vol. 895. Cham: Springer International Publishing, 2019. https:\/\/doi.org\/10.1007\/978-3-030-05532-5.","DOI":"10.1007\/978-3-030-05532-5"},{"key":"1106_CR32","doi-asserted-by":"publisher","unstructured":"M. Aljanabi, Y. E. \u00d6zok, J. Rahebi, and A. S. Abdullah, \u201cSkin lesion segmentation method for dermoscopy images using artificial bee colony algorithm,\u201d Symmetry (Basel), vol. 10, no. 8, 2018. https:\/\/doi.org\/10.3390\/sym10080347.","DOI":"10.3390\/sym10080347"},{"key":"1106_CR33","doi-asserted-by":"publisher","unstructured":"S. S. Chouhan, A. Kaul, and U. P. Singh, Soft computing approaches for image segmentation: a survey, vol. 77, no. 21. Multimedia Tools and Applications, 2018. https:\/\/doi.org\/10.1007\/s11042-018-6005-6.","DOI":"10.1007\/s11042-018-6005-6"},{"key":"1106_CR34","doi-asserted-by":"publisher","unstructured":"M. Waghulde, S. Kulkarni, and G. Phadke, \u201cDetection of Skin Cancer Lesions from Digital Images with Image Processing Techniques,\u201d in 2019 IEEE Pune Section International Conference (PuneCon), IEEE, Dec. 2019, pp. 1\u20136. https:\/\/doi.org\/10.1109\/PuneCon46936.2019.9105886.","DOI":"10.1109\/PuneCon46936.2019.9105886"},{"issue":"June","key":"1106_CR35","doi-asserted-by":"publisher","first-page":"114822","DOI":"10.1109\/ACCESS.2020.3003890","volume":"8","author":"MA Kassem","year":"2020","unstructured":"M. A. Kassem, K. M. Hosny, and M. M. Fouad, \u201cSkin Lesions Classification into Eight Classes for ISIC 2019 Using Deep Convolutional Neural Network and Transfer Learning,\u201d IEEE Access, vol. 8, no. June, pp. 114822\u2013114832, 2020. https:\/\/doi.org\/10.1109\/ACCESS.2020.3003890.","journal-title":"IEEE Access"},{"key":"1106_CR36","doi-asserted-by":"publisher","unstructured":"N. Kavitha and M. Vayelapelli, \u201cA Study on Pre-processing Techniques for Automated Skin Cancer Detection,\u201d 2020, pp. 145\u2013153. https:\/\/doi.org\/10.1007\/978-981-15-2407-3_19.","DOI":"10.1007\/978-981-15-2407-3_19"},{"key":"1106_CR37","doi-asserted-by":"publisher","unstructured":"M. Krishna Monika, N. Arun Vignesh, C. Usha Kumari, M. N. V. S. S. Kumar, and E. Laxmi Lydia, \u201cSkin cancer detection and classification using machine learning,\u201d Mater Today Proc, vol. 33, no. xxxx, pp. 4266\u20134270, 2020. https:\/\/doi.org\/10.1016\/j.matpr.2020.07.366.","DOI":"10.1016\/j.matpr.2020.07.366"},{"key":"1106_CR38","doi-asserted-by":"publisher","unstructured":"\u00c7. Kaymak and A. U\u00e7ar, A brief survey and an application of semantic image segmentation for autonomous driving, vol. 136. 2019. https:\/\/doi.org\/10.1007\/978-3-030-11479-4_9.","DOI":"10.1007\/978-3-030-11479-4_9"},{"key":"1106_CR39","doi-asserted-by":"publisher","unstructured":"R. Mohakud and R. Dash, \u201cSkin cancer image segmentation utilizing a novel EN-GWO based hyper-parameter optimized FCEDN,\u201d Journal of King Saud University - Computer and Information Sciences, no. xxxx, 2022. https:\/\/doi.org\/10.1016\/j.jksuci.2021.12.018.","DOI":"10.1016\/j.jksuci.2021.12.018"},{"key":"1106_CR40","doi-asserted-by":"publisher","unstructured":"A. Murugan, S. A. H. Nair, A. A. P. Preethi, and K. P. S. Kumar, \u201cDiagnosis of skin cancer using machine learning techniques,\u201d Microprocess Microsyst, vol. 81, no. October 2020, p. 103727, 2021. https:\/\/doi.org\/10.1016\/j.micpro.2020.103727.","DOI":"10.1016\/j.micpro.2020.103727"},{"key":"1106_CR41","doi-asserted-by":"publisher","first-page":"2021","DOI":"10.1088\/1742-6596\/1916\/1\/012148","volume":"1","author":"A Pushpalatha","year":"1916","unstructured":"A. Pushpalatha, P. Dharani, R. Dharini, and J. Gowsalya, \u201cRetraction: Skin Cancer Classification Detection using CNN and SVM,\u201d J Phys Conf Ser, vol. 1916, no. 1, 2021. https:\/\/doi.org\/10.1088\/1742-6596\/1916\/1\/012148.","journal-title":"J Phys Conf Ser"},{"key":"1106_CR42","doi-asserted-by":"publisher","first-page":"118198","DOI":"10.1109\/ACCESS.2022.3220329","volume":"10","author":"A Imran","year":"2022","unstructured":"A. Imran, A. Nasir, M. Bilal, G. Sun, A. Alzahrani, and A. Almuhaimeed, \u201cSkin Cancer Detection Using Combined Decision of Deep Learners,\u201d IEEE Access, vol. 10, pp. 118198\u2013118212, 2022. https:\/\/doi.org\/10.1109\/ACCESS.2022.3220329.","journal-title":"IEEE Access"},{"key":"1106_CR43","doi-asserted-by":"publisher","unstructured":"T. Guergueb and M. A. Akhloufi, \u201cSkin Cancer Detection using Ensemble Learning and Grouping of Deep Models,\u201d in International Conference on Content-based Multimedia Indexing, New York, NY, USA: ACM, Sep. 2022, pp. 121\u2013125. https:\/\/doi.org\/10.1145\/3549555.3549584.","DOI":"10.1145\/3549555.3549584"},{"key":"1106_CR44","doi-asserted-by":"publisher","unstructured":"S. S. Sundari, Dr. S. AK, and Dr. M. Islabudeen, \u201cSkin Lesions Detection using Deep Learning Techniques,\u201d Int J Res Appl Sci Eng Technol, vol. 11, no. 5, pp. 2546\u20132548, May 2023. https:\/\/doi.org\/10.22214\/ijraset.2023.52129.","DOI":"10.22214\/ijraset.2023.52129"},{"key":"1106_CR45","doi-asserted-by":"publisher","unstructured":"A. Boudhir Abdelhakim, B. Ahmed Mohamed, and D. Yousra, \u201cA New Approach using Deep Learning and Reinforcement Learning in HealthCare,\u201d International journal of electrical and computer engineering systems, vol. 14, no. 5, pp. 557\u2013564, Jun. 2023. https:\/\/doi.org\/10.32985\/ijeces.14.5.7.","DOI":"10.32985\/ijeces.14.5.7"},{"key":"1106_CR46","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-14697-3","author":"JV Tembhurne","year":"2023","unstructured":"J. V. Tembhurne, N. Hebbar, H. Y. Patil, and T. Diwan, \u201cSkin cancer detection using ensemble of machine learning and deep learning techniques,\u201d Multimed Tools Appl, Jul. 2023. https:\/\/doi.org\/10.1007\/s11042-023-14697-3.","journal-title":"Multimed Tools Appl"},{"issue":"19","key":"1106_CR47","doi-asserted-by":"publisher","first-page":"6578","DOI":"10.1016\/j.eswa.2015.04.034","volume":"42","author":"I Giotis","year":"2015","unstructured":"I. Giotis, N. Molders, S. Land, M. Biehl, M. F. Jonkman, and N. Petkov, \u201cMED-NODE: A computer-assisted melanoma diagnosis system using non-dermoscopic images,\u201d Expert Syst Appl, vol. 42, no. 19, pp. 6578\u20136585, 2015. https:\/\/doi.org\/10.1016\/j.eswa.2015.04.034.","journal-title":"Expert Syst Appl"},{"issue":"4","key":"1106_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-016-0460-2","volume":"40","author":"J Premaladha","year":"2016","unstructured":"J. Premaladha and K. S. Ravichandran, \u201cNovel Approaches for Diagnosing Melanoma Skin Lesions Through Supervised and Deep Learning Algorithms,\u201d J Med Syst, vol. 40, no. 4, pp. 1\u201312, 2016. https:\/\/doi.org\/10.1007\/s10916-016-0460-2.","journal-title":"J Med Syst"},{"key":"1106_CR49","doi-asserted-by":"publisher","unstructured":"T. Y. Satheesha, D. Satyanarayana, M. N. G. Prasad, and K. D. Dhruve, \u201cMelanoma Is Skin Deep: A 3D Reconstruction Technique for Computerized Dermoscopic Skin Lesion Classification,\u201d IEEE J Transl Eng Health Med, vol. 5, no. c, 2017. https:\/\/doi.org\/10.1109\/JTEHM.2017.2648797.","DOI":"10.1109\/JTEHM.2017.2648797"},{"issue":"9","key":"1106_CR50","doi-asserted-by":"publisher","first-page":"1876","DOI":"10.1109\/TMI.2017.2695227","volume":"36","author":"Y Yuan","year":"2017","unstructured":"Y. Yuan, M. Chao, and Y. C. Lo, \u201cAutomatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks with Jaccard Distance,\u201d IEEE Trans Med Imaging, vol. 36, no. 9, pp. 1876\u20131886, 2017. https:\/\/doi.org\/10.1109\/TMI.2017.2695227.","journal-title":"IEEE Trans Med Imaging"},{"issue":"2","key":"1106_CR51","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1111\/srt.12622","volume":"25","author":"N Singh","year":"2019","unstructured":"N. Singh and S. K. Gupta, \u201cRecent advancement in the early detection of melanoma using computerized tools: An image analysis perspective,\u201d Skin Research and Technology, vol. 25, no. 2, pp. 129\u2013141, 2019. https:\/\/doi.org\/10.1111\/srt.12622.","journal-title":"Skin Research and Technology"},{"issue":"5","key":"1106_CR52","doi-asserted-by":"publisher","first-page":"1555","DOI":"10.31557\/APJCP.2019.20.5.1555","volume":"20","author":"RD Seeja","year":"2019","unstructured":"R. D. Seeja and A. Suresh, \u201cDeep learning based skin lesion segmentation and classification of melanoma using support vector machine (SVM),\u201d Asian Pacific Journal of Cancer Prevention, vol. 20, no. 5, pp. 1555\u20131561, 2019. https:\/\/doi.org\/10.31557\/APJCP.2019.20.5.1555.","journal-title":"Asian Pacific Journal of Cancer Prevention"},{"issue":"6","key":"1106_CR53","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/s20061601","volume":"20","author":"K Zafar","year":"2020","unstructured":"K. Zafar et al., \u201cSkin lesion segmentation from dermoscopic images using convolutional neural network,\u201d Sensors (Switzerland), vol. 20, no. 6, pp. 1\u201314, 2020. https:\/\/doi.org\/10.3390\/s20061601.","journal-title":"Sensors (Switzerland)"},{"key":"1106_CR54","doi-asserted-by":"publisher","unstructured":"R. Vani, J. C. Kavitha, and D. Subitha, \u201cNovel approach for melanoma detection through iterative deep vector network,\u201d J Ambient Intell Humaniz Comput, no. 2018, 2021. https:\/\/doi.org\/10.1007\/s12652-021-03242-5.","DOI":"10.1007\/s12652-021-03242-5"},{"issue":"1","key":"1106_CR55","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-07885-y","volume":"12","author":"H Ashraf","year":"2022","unstructured":"H. Ashraf, A. Waris, M. F. Ghafoor, S. O. Gilani, and I. K. Niazi, \u201cMelanoma segmentation using deep learning with test-time augmentations and conditional random fields,\u201d Sci Rep, vol. 12, no. 1, pp. 1\u201316, 2022. https:\/\/doi.org\/10.1038\/s41598-022-07885-y.","journal-title":"Sci Rep"},{"key":"1106_CR56","doi-asserted-by":"publisher","unstructured":"M. Tahir, A. Naeem, H. Malik, J. Tanveer, R. A. Naqvi, and S. W. Lee, \u201cDSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images,\u201d Cancers (Basel), vol. 15, no. 7, Apr. 2023. https:\/\/doi.org\/10.3390\/cancers15072179.","DOI":"10.3390\/cancers15072179"},{"issue":"2","key":"1106_CR57","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1080\/21681163.2015.1029080","volume":"5","author":"M Ruela","year":"2017","unstructured":"M. Ruela, C. Barata, J. S. Marques, and J. Rozeira, \u201cA system for the detection of melanomas in dermoscopy images using shape and symmetry features,\u201d Comput Methods Biomech Biomed Eng Imaging Vis, vol. 5, no. 2, pp. 127\u2013137, 2017. https:\/\/doi.org\/10.1080\/21681163.2015.1029080.","journal-title":"Comput Methods Biomech Biomed Eng Imaging Vis"},{"issue":"3","key":"1106_CR58","doi-asserted-by":"publisher","first-page":"965","DOI":"10.1109\/JSYST.2013.2271540","volume":"8","author":"C Barata","year":"2014","unstructured":"C. Barata, M. Ruela, M. Francisco, T. Mendonca, and J. S. Marques, \u201cTwo systems for the detection of melanomas in dermoscopy images using texture and color features,\u201d IEEE Syst J, vol. 8, no. 3, pp. 965\u2013979, 2014. https:\/\/doi.org\/10.1109\/JSYST.2013.2271540.","journal-title":"IEEE Syst J"},{"key":"1106_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2021.100819","volume":"28","author":"MK Hasan","year":"2022","unstructured":"M. K. Hasan, M. T. E. Elahi, M. A. Alam, M. T. Jawad, and R. Mart\u00ed, \u201cDermoExpert: Skin lesion classification using a hybrid convolutional neural network through segmentation, transfer learning, and augmentation,\u201d Inform Med Unlocked, vol. 28, p. 100819, 2022. https:\/\/doi.org\/10.1016\/j.imu.2021.100819.","journal-title":"Inform Med Unlocked"},{"issue":"10","key":"1106_CR60","doi-asserted-by":"publisher","first-page":"2744","DOI":"10.1109\/TBME.2012.2209423","volume":"59","author":"C Barata","year":"2012","unstructured":"C. Barata, J. S. Marques, and J. Rozeira, \u201cA system for the detection of pigment network in dermoscopy images using directional filters,\u201d IEEE Trans Biomed Eng, vol. 59, no. 10, pp. 2744\u20132754, 2012. https:\/\/doi.org\/10.1109\/TBME.2012.2209423.","journal-title":"IEEE Trans Biomed Eng"},{"key":"1106_CR61","doi-asserted-by":"publisher","unstructured":"P. Dubai, S. Bhatt, C. Joglekar, and S. Patii, \u201cSkin cancer detection and classification,\u201d Proceedings of the 2017 6th International Conference on Electrical Engineering and Informatics: Sustainable Society Through Digital Innovation, ICEEI 2017, vol. 2017-Novem, pp. 1\u20136, 2018. https:\/\/doi.org\/10.1109\/ICEEI.2017.8312419.","DOI":"10.1109\/ICEEI.2017.8312419"},{"key":"1106_CR62","doi-asserted-by":"publisher","DOI":"10.1016\/j.imed.2022.08.004","author":"H Bhatt","year":"2022","unstructured":"H. Bhatt, V. Shah, K. Shah, R. Shah, and M. Shah, \u201cState-of-the-art machine learning techniques for melanoma skin cancer detection and classification: a comprehensive review,\u201d Intelligent Medicine, 2022. https:\/\/doi.org\/10.1016\/j.imed.2022.08.004.","journal-title":"Intelligent Medicine"},{"key":"1106_CR63","doi-asserted-by":"publisher","unstructured":"N. C. F. Codella et al., \u201cSkin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC),\u201d Proceedings - International Symposium on Biomedical Imaging, vol. 2018-April, no. Isbi, pp. 168\u2013172, 2018. https:\/\/doi.org\/10.1109\/ISBI.2018.8363547.","DOI":"10.1109\/ISBI.2018.8363547"},{"issue":"24","key":"1106_CR64","doi-asserted-by":"publisher","first-page":"7080","DOI":"10.3390\/s20247080","volume":"20","author":"J Wu","year":"2020","unstructured":"J. Wu, W. Hu, Y. Wen, W. Tu, and X. Liu, \u201cSkin Lesion Classification Using Densely Connected Convolutional Networks with Attention Residual Learning,\u201d Sensors, vol. 20, no. 24, p. 7080, Dec. 2020. https:\/\/doi.org\/10.3390\/s20247080.","journal-title":"Sensors"},{"key":"1106_CR65","doi-asserted-by":"publisher","unstructured":"M. F. Jojoa Acosta, L. Y. Caballero Tovar, M. B. Garcia-Zapirain, and W. S. Percybrooks, \u201cMelanoma diagnosis using deep learning techniques on dermatoscopic images,\u201d BMC Med Imaging, vol. 21, no. 1, p. 6, Dec. 2021. https:\/\/doi.org\/10.1186\/s12880-020-00534-8.","DOI":"10.1186\/s12880-020-00534-8"},{"key":"1106_CR66","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102305","volume":"75","author":"B Cassidy","year":"2022","unstructured":"B. Cassidy, C. Kendrick, A. Brodzicki, J. Jaworek-Korjakowska, and M. H. Yap, \u201cAnalysis of the ISIC image datasets: Usage, benchmarks and recommendations,\u201d Med Image Anal, vol. 75, p. 102305, Jan. 2022. https:\/\/doi.org\/10.1016\/j.media.2021.102305.","journal-title":"Med Image Anal"},{"issue":"18","key":"1106_CR67","doi-asserted-by":"publisher","first-page":"27501","DOI":"10.1007\/s11042-023-14697-3","volume":"82","author":"JV Tembhurne","year":"2023","unstructured":"J. V. Tembhurne, N. Hebbar, H. Y. Patil, and T. Diwan, \u201cSkin cancer detection using ensemble of machine learning and deep learning techniques,\u201d Multimed Tools Appl, vol. 82, no. 18, pp. 27501\u201327524, Jul. 2023. https:\/\/doi.org\/10.1007\/s11042-023-14697-3.","journal-title":"Multimed Tools Appl"},{"key":"1106_CR68","doi-asserted-by":"publisher","unstructured":"M. K. Hasan, M. A. Ahamad, C. H. Yap, and G. Yang, \u201cA survey, review, and future trends of skin lesion segmentation and classification,\u201d Computers in Biology and Medicine, vol. 155. Elsevier Ltd, Mar. 01, 2023. https:\/\/doi.org\/10.1016\/j.compbiomed.2023.106624.","DOI":"10.1016\/j.compbiomed.2023.106624"},{"key":"1106_CR69","doi-asserted-by":"publisher","unstructured":"S. L. Lee and C. C. Tseng, \u201cImage enhancement using DCT-based matrix homomorphic filtering method,\u201d 2016 IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2016, pp. 1\u20134, 2017. https:\/\/doi.org\/10.1109\/APCCAS.2016.7803880.","DOI":"10.1109\/APCCAS.2016.7803880"},{"key":"1106_CR70","doi-asserted-by":"publisher","unstructured":"T. F. Sanam and H. Imtiaz, \u201cA DCT-based noisy speech enhancement method using teager energy operator,\u201d Proceedings of the 2013 5th International Conference on Knowledge and Smart Technology, KST 2013, pp. 16\u201320, 2013. https:\/\/doi.org\/10.1109\/KST.2013.6512780.","DOI":"10.1109\/KST.2013.6512780"},{"key":"1106_CR71","unstructured":"R. Rajagopal, \u201cCh01-P373624.tex Discrete Cosine and Sine Transforms 1.1 Introduction,\u201d 2006."},{"key":"1106_CR72","doi-asserted-by":"publisher","unstructured":"U. Saghir and S. K. Singh, \u201cSegmentation of Skin Cancer Images Applying Background Subtraction with Midpoint Analysis,\u201d 2024. https:\/\/doi.org\/10.1201\/9781003405580-93.","DOI":"10.1201\/9781003405580-93"},{"issue":"6","key":"1106_CR73","doi-asserted-by":"publisher","first-page":"1809","DOI":"10.1007\/s00500-018-3540-z","volume":"23","author":"PA Flores-Vidal","year":"2019","unstructured":"P. A. Flores-Vidal, P. Olaso, D. G\u00f3mez, and C. Guada, \u201cA new edge detection method based on global evaluation using fuzzy clustering,\u201d Soft comput, vol. 23, no. 6, pp. 1809\u20131821, 2019. https:\/\/doi.org\/10.1007\/s00500-018-3540-z.","journal-title":"Soft comput"},{"key":"1106_CR74","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-14409-x","author":"U Saghir","year":"2023","unstructured":"U. Saghir and M. Hasan, \u201cSkin cancer detection and classification based on differential analyzer algorithm,\u201d Multimed Tools Appl, 2023. https:\/\/doi.org\/10.1007\/s11042-023-14409-x.","journal-title":"Multimed Tools Appl"},{"key":"1106_CR75","doi-asserted-by":"publisher","unstructured":"A. Blundo, A. Cignoni, T. Banfi, and G. Ciuti, \u201cComparative Analysis of Diagnostic Techniques for Melanoma Detection: A Systematic Review of Diagnostic Test Accuracy Studies and Meta-Analysis,\u201d Front Med (Lausanne), vol. 8, no. April, 2021. https:\/\/doi.org\/10.3389\/fmed.2021.637069.","DOI":"10.3389\/fmed.2021.637069"},{"key":"1106_CR76","doi-asserted-by":"publisher","unstructured":"J. Jaworek-Korjakowska, \u201cComputer-aided diagnosis of micro-malignant melanoma lesions applying support vector machines,\u201d Biomed Res Int, vol. 2016, 2016. https:\/\/doi.org\/10.1155\/2016\/4381972.","DOI":"10.1155\/2016\/4381972"},{"issue":"2","key":"1106_CR77","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1016\/j.jksuci.2023.01.014","volume":"35","author":"A Mohammed","year":"2023","unstructured":"A. Mohammed and R. Kora, \u201cA comprehensive review on ensemble deep learning: Opportunities and challenges,\u201d Journal of King Saud University - Computer and Information Sciences, vol. 35, no. 2, pp. 757\u2013774, Feb. 2023. https:\/\/doi.org\/10.1016\/j.jksuci.2023.01.014.","journal-title":"Journal of King Saud University - Computer and Information Sciences"},{"key":"1106_CR78","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.cmpb.2016.03.032","volume":"131","author":"RB Oliveira","year":"2016","unstructured":"R. B. Oliveira, M. E. Filho, Z. Ma, J. P. Papa, A. S. Pereira, and J. M. R. S. Tavares, \u201cComputational methods for the image segmentation of pigmented skin lesions: A review,\u201d Comput Methods Programs Biomed, vol. 131, pp. 127\u2013141, 2016. https:\/\/doi.org\/10.1016\/j.cmpb.2016.03.032.","journal-title":"Comput Methods Programs Biomed"},{"key":"1106_CR79","doi-asserted-by":"publisher","unstructured":"A. Murugan, S. A. H. Nair, and K. P. S. Kumar, \u201cDetection of Skin Cancer Using SVM, Random Forest and kNN Classifiers,\u201d J Med Syst, vol. 43, no. 8, 2019. https:\/\/doi.org\/10.1007\/s10916-019-1400-8.","DOI":"10.1007\/s10916-019-1400-8"},{"key":"1106_CR80","doi-asserted-by":"publisher","unstructured":"A. Bassel, A. B. Abdulkareem, Z. A. A. Alyasseri, N. S. Sani, and H. J. Mohammed, \u201cAutomatic Malignant and Benign Skin Cancer Classification Using a Hybrid Deep Learning Approach,\u201d Diagnostics, vol. 12, no. 10, 2022. https:\/\/doi.org\/10.3390\/diagnostics12102472.","DOI":"10.3390\/diagnostics12102472"},{"key":"1106_CR81","doi-asserted-by":"publisher","unstructured":"X. Wang, \u201cDeep Learning-based and Machine Learning-based Application in Skin Cancer Image Classification,\u201d J Phys Conf Ser, vol. 2405, no. 1, 2022. https:\/\/doi.org\/10.1088\/1742-6596\/2405\/1\/012024.","DOI":"10.1088\/1742-6596\/2405\/1\/012024"},{"key":"1106_CR82","doi-asserted-by":"publisher","DOI":"10.1109\/ODICON54453.2022.10009956","author":"J Das","year":"2022","unstructured":"J. Das, D. Mishra, A. Das, M. Mohanty, and A. Sarangi, Skin cancer detection using machine learning techniques with ABCD features. 2022. https:\/\/doi.org\/10.1109\/ODICON54453.2022.10009956.","journal-title":"Skin cancer detection using machine learning techniques with ABCD features."},{"key":"1106_CR83","doi-asserted-by":"publisher","unstructured":"D. Keerthana, V. Venugopal, M. K. Nath, and M. Mishra, \u201cHybrid convolutional neural networks with SVM classifier for classification of skin cancer,\u201d Biomedical Engineering Advances, vol. 5, no. December 2022, p. 100069, 2023. https:\/\/doi.org\/10.1016\/j.bea.2022.100069.","DOI":"10.1016\/j.bea.2022.100069"},{"issue":"9","key":"1106_CR84","doi-asserted-by":"publisher","first-page":"2092","DOI":"10.1109\/TMI.2019.2893944","volume":"38","author":"J Zhang","year":"2019","unstructured":"J. Zhang, Y. Xie, Y. Xia, and C. Shen, \u201cAttention Residual Learning for Skin Lesion Classification,\u201d IEEE Trans Med Imaging, vol. 38, no. 9, pp. 2092\u20132103, Sep. 2019. https:\/\/doi.org\/10.1109\/TMI.2019.2893944.","journal-title":"IEEE Trans Med Imaging"},{"key":"1106_CR85","doi-asserted-by":"publisher","unstructured":"M. Hasan, S. Das Barman, S. Islam, and A. W. Reza, \u201cSkin cancer detection using convolutional neural network,\u201d in ACM International Conference Proceeding Series, Association for Computing Machinery, Apr. 2019, pp. 254\u2013258. https:\/\/doi.org\/10.1145\/3330482.3330525.","DOI":"10.1145\/3330482.3330525"},{"key":"1106_CR86","doi-asserted-by":"publisher","unstructured":"A. Javaid, M. Sadiq, and F. Akram, \u201cSkin Cancer Classification Using Image Processing and Machine Learning,\u201d in 2021 International Bhurban Conference on Applied Sciences and Technologies (IBCAST), IEEE, Jan. 2021, pp. 439\u2013444. https:\/\/doi.org\/10.1109\/IBCAST51254.2021.9393198.","DOI":"10.1109\/IBCAST51254.2021.9393198"},{"issue":"7","key":"1106_CR87","doi-asserted-by":"publisher","first-page":"1183","DOI":"10.3390\/healthcare10071183","volume":"10","author":"W Gouda","year":"2022","unstructured":"W. Gouda, N. U. Sama, G. Al-Waakid, M. Humayun, and N. Z. Jhanjhi, \u201cDetection of Skin Cancer Based on Skin Lesion Images Using Deep Learning,\u201d Healthcare, vol. 10, no. 7, p. 1183, Jun. 2022. https:\/\/doi.org\/10.3390\/healthcare10071183.","journal-title":"Healthcare"},{"issue":"3","key":"1106_CR88","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1007\/s11517-021-02473-0","volume":"60","author":"HC Reis","year":"2022","unstructured":"H. C. Reis, V. Turk, K. Khoshelham, and S. Kaya, \u201cInSiNet: a deep convolutional approach to skin cancer detection and segmentation,\u201d Med Biol Eng Comput, vol. 60, no. 3, pp. 643\u2013662, Mar. 2022. https:\/\/doi.org\/10.1007\/s11517-021-02473-0.","journal-title":"Med Biol Eng Comput"},{"key":"1106_CR89","doi-asserted-by":"publisher","DOI":"10.1016\/j.bea.2022.100069","volume":"5","author":"D Keerthana","year":"2023","unstructured":"D. Keerthana, V. Venugopal, M. K. Nath, and M. Mishra, \u201cHybrid convolutional neural networks with SVM classifier for classification of skin cancer,\u201d Biomedical Engineering Advances, vol. 5, p. 100069, Jun. 2023. https:\/\/doi.org\/10.1016\/j.bea.2022.100069.","journal-title":"Biomedical Engineering Advances"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-01106-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-024-01106-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-01106-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T08:31:53Z","timestamp":1730190713000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-024-01106-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,16]]},"references-count":89,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["1106"],"URL":"https:\/\/doi.org\/10.1007\/s10278-024-01106-w","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,16]]},"assertion":[{"value":"6 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 April 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}