{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T14:47:37Z","timestamp":1774968457638,"version":"3.50.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,5,28]],"date-time":"2024-05-28T00:00:00Z","timestamp":1716854400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,28]],"date-time":"2024-05-28T00:00:00Z","timestamp":1716854400000},"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":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-024-02949-4","type":"journal-article","created":{"date-parts":[[2024,5,28]],"date-time":"2024-05-28T17:38:51Z","timestamp":1716917931000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Automatic Classification of Melanoma Using Grab-Cut Segmentation & Convolutional Neural Network"],"prefix":"10.1007","volume":"5","author":[{"given":"Sarvachan","family":"Verma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manoj","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,28]]},"reference":[{"key":"2949_CR1","unstructured":"Cancer Research UK. 2012. Cancer World Wide - the global picture. Retrieved March 16, 2019 from https:\/\/www.cancerresearchuk.org\/health-professional\/cancer-statistics\/worldwide-cancer. Accessed 5 Jan 2023."},{"key":"2949_CR2","unstructured":"World Health Organization. 2019. Skin Cancer. Retrieved March 16, 2019 from http:\/\/www.who.int\/en\/."},{"key":"2949_CR3","doi-asserted-by":"publisher","first-page":"5172","DOI":"10.3390\/s21155172","volume":"21","author":"Y Dong","year":"2021","unstructured":"Dong Y, Wang L, Cheng S, Li Y. FAC-Net: feedback attention network based on context encoder network for skin lesion segmentation. Sensors. 2021;21:5172.","journal-title":"Sensors"},{"key":"2949_CR4","doi-asserted-by":"publisher","first-page":"1081","DOI":"10.1001\/jamadermatol.2015.1187","volume":"151","author":"HW Rogers","year":"2015","unstructured":"Rogers HW, Weinstock MA, Feldman SR, Coldiron BM. Incidence estimate of nonmelanoma skin cancer (keratinocyte carcinomas) in the us population, 2012. JAMA Dermatol. 2015;151:1081\u20136.","journal-title":"JAMA Dermatol"},{"key":"2949_CR5","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1001\/archdermatol.2010.4","volume":"146","author":"RS Stern","year":"2010","unstructured":"Stern RS. Prevalence of a history of skin cancer in 2007: results of an incidence-based model. Arch Dermatol. 2010;146:279\u201382.","journal-title":"Arch Dermatol"},{"key":"2949_CR6","doi-asserted-by":"publisher","first-page":"5:1","DOI":"10.1147\/JRD.2017.2708299","volume":"61","author":"NC Codella","year":"2017","unstructured":"Codella NC, Nguyen Q-B, Pankanti S, Gutman DA, Helba B, Halpern AC, Smith J. R Deep learning ensembles for melanoma recognition in dermoscopy images. IBM J Res Dev. 2017;61:5:1-5:15.","journal-title":"IBM J Res Dev"},{"key":"2949_CR7","doi-asserted-by":"publisher","first-page":"7","DOI":"10.14740\/wjon1349","volume":"12","author":"PP Naik","year":"2021","unstructured":"Naik PP. Cutaneous malignant melanoma: a review of early diagnosis and management. World J Oncol. 2021;12:7.","journal-title":"World J Oncol"},{"key":"2949_CR8","doi-asserted-by":"publisher","first-page":"7286","DOI":"10.3390\/s21217286","volume":"21","author":"M Alhaisoni","year":"2021","unstructured":"Alhaisoni M, Tariq U, Hussain N, Majid A, Dama\u0161evi\u02c7cius R, Maskeli\u016bnas R. COVID-19 case recognition from chest CT images by deep learning, entropy-controlled firefly optimization, and parallel feature fusion. Sensors. 2021;21:7286.","journal-title":"Sensors"},{"key":"2949_CR9","first-page":"1","volume":"36","author":"MA Khan","year":"2021","unstructured":"Khan MA, Muhammad K, Sharif M, Akram T, Kadry S. Intelligent fusion-assisted skin lesion localization and classification for smart healthcare. Neural Comput Appl. 2021;36:1\u201316.","journal-title":"Neural Comput Appl"},{"key":"2949_CR10","doi-asserted-by":"publisher","first-page":"811","DOI":"10.1007\/s10462-020-09865-y","volume":"54","author":"A Adegun","year":"2020","unstructured":"Adegun A, Viriri S. Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-art. Artif Intell Rev. 2020;54:811\u201341.","journal-title":"Artif Intell Rev"},{"key":"2949_CR11","first-page":"1","volume":"13","author":"Y Ren","year":"2021","unstructured":"Ren Y, Yu L, Tian S, Cheng J, Guo Z, Zhang Y. Serial attention network for skin lesion segmentation. J Ambient Intell Humaniz Comput. 2021;13:1\u201312.","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"2949_CR12","doi-asserted-by":"publisher","first-page":"1006","DOI":"10.1109\/TBME.2018.2866166","volume":"66","author":"Z Yu","year":"2018","unstructured":"Yu Z, Jiang X, Zhou F, Qin J, Ni D, Chen S, Lei B, Wang T. Melanoma recognition in dermoscopy images via aggregated deep convolutional features. IEEE Trans Biomed Eng. 2018;66:1006\u201316.","journal-title":"IEEE Trans Biomed Eng"},{"key":"2949_CR13","doi-asserted-by":"crossref","unstructured":"Aziz S, Bilal M, Khan MU, Amjad F. Deep learning-based automatic morphological classification of leukocytes using blood smears. In: Proceedings of the 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), Istanbul, Turkey, 14\u201315 April 2020; pp. 1\u20135.","DOI":"10.1109\/ICECCE49384.2020.9179246"},{"key":"2949_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2020.106956","volume":"90","author":"MA Khan","year":"2021","unstructured":"Khan MA, Zhang Y-D, Sharif M, Akram T. Pixels to classes: intelligent learning framework for multiclass skin lesion localization and classification. Comput Electr Eng. 2021;90: 106956.","journal-title":"Comput Electr Eng"},{"key":"2949_CR15","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1109\/JBHI.2018.2825251","volume":"23","author":"F Navarro","year":"2018","unstructured":"Navarro F, Escudero-Vinolo M, Bescos J. Accurate segmentation and registration of skin lesion images to evaluate lesion change. IEEE J Biomed Health Inform. 2018;23:501\u20138.","journal-title":"IEEE J Biomed Health Inform"},{"key":"2949_CR16","doi-asserted-by":"crossref","unstructured":"Hosny KM., Kassem MA, Foaud MM. Skin cancer classification using deep learning and transfer learning. In: Proceedings of the 2018 9th Cairo International Biomedical Engineering Conference (CIBEC), Cairo, Egypt, 20\u201322 December 2018; pp. 90\u201393.","DOI":"10.1109\/CIBEC.2018.8641762"},{"key":"2949_CR17","doi-asserted-by":"crossref","unstructured":"Vidya M, Karki MV. Skin cancer detection using machine learning techniques. In: 2020 IEEE International Conference on electronics, computing and communication technologies (CONECCT). IEEE, 2020; pp. 1\u20135.","DOI":"10.1109\/CONECCT50063.2020.9198489"},{"key":"2949_CR18","doi-asserted-by":"crossref","unstructured":"Pham TC, Tran GS, Nghiem TP, Doucet A, Luong CM, Hoang V-D. A comparative study for classification of skin cancer. In: 2019 International Conference on System Science and Engineering (ICSSE). IEEE, 2019; pp. 267\u2013272","DOI":"10.1109\/ICSSE.2019.8823124"},{"key":"2949_CR19","doi-asserted-by":"crossref","unstructured":"Mhaske H, Phalke D. Melanoma skin cancer detection and classification based on supervised and unsupervised learning. In: 2013 International conference on Circuits, Controls and Communications (CCUBE). IEEE, 2013; pp. 1\u20135.","DOI":"10.1109\/CCUBE.2013.6718539"},{"issue":"2","key":"2949_CR20","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.compmedimag.2010.08.001","volume":"35","author":"R Garnavi","year":"2011","unstructured":"Garnavi R, Aldeen M, Celebi ME, Varigos G, Finch S. Border detection in dermoscopy images using hybrid thresholding on optimized color channels. Comput Med Imaging Graph. 2011;35(2):105\u201315.","journal-title":"Comput Med Imaging Graph"},{"issue":"5","key":"2949_CR21","doi-asserted-by":"publisher","first-page":"2677","DOI":"10.3390\/app12052677","volume":"12","author":"L Hoang","year":"2022","unstructured":"Hoang L, Lee SH, Lee EJ, Kwon KR. Multiclass skin lesion classification using a novel lightweight deep learning framework for Smart healthcare. Appl Sci. 2022;12(5):2677.","journal-title":"Appl Sci"},{"key":"2949_CR22","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2018.161","volume":"5","author":"P Tschandl","year":"2018","unstructured":"Tschandl P, Rosendahl C, Kittler H. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci Data. 2018;5: 180161.","journal-title":"Sci Data"},{"key":"2949_CR23","unstructured":"A. Swain, Noise filtering in digital image processing, medium, 3, 2018, [Online]. Available: https:\/\/medium.com\/image-vision\/noise-filtering-in-digitalimage-processing-d12b5266847c. Accessed 20 Dec 2020."},{"key":"2949_CR24","doi-asserted-by":"publisher","first-page":"112193","DOI":"10.1109\/ACCESS.2021.3103410","volume":"9","author":"K Thurnhofer-Hemsi","year":"2021","unstructured":"Thurnhofer-Hemsi K, L\u00f3pez-Rubio E, Dom\u00ednguez E, Elizondo DA. Skin lesion classification by ensembles of deep convolutional networks and regularly spaced shifting. IEEE Access. 2021;9:112193\u2013205.","journal-title":"IEEE Access"},{"issue":"39","key":"2949_CR25","doi-asserted-by":"publisher","first-page":"28477","DOI":"10.1007\/s11042-020-09388-2","volume":"79","author":"SS Chaturvedi","year":"2020","unstructured":"Chaturvedi SS, Tembhurne JV, Diwan T. A multi-class skin cancer classification using deep convolutional neural networks. Multimed Tools Appl. 2020;79(39):28477\u201398.","journal-title":"Multimed Tools Appl"},{"issue":"8","key":"2949_CR26","doi-asserted-by":"publisher","first-page":"2852","DOI":"10.3390\/s21082852","volume":"21","author":"PN Srinivasu","year":"2021","unstructured":"Srinivasu PN, et al. Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM. Sensors. 2021;21(8):2852.","journal-title":"Sensors"},{"issue":"29e3","key":"2949_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4108\/eai.13-10-2021.171318","volume":"8","author":"N Sindhwani","year":"2021","unstructured":"Sindhwani N, Yadav V, et al. Performance analysis of deep neural networks using computer vision. EAI Endorsed Trans Ind Netw Intell Syst. 2021;8(29e3):1\u201311. https:\/\/doi.org\/10.4108\/eai.13-10-2021.171318.","journal-title":"EAI Endorsed Trans Ind Netw Intell Syst"},{"issue":"5","key":"2949_CR28","doi-asserted-by":"publisher","first-page":"390","DOI":"10.2174\/2352096515666220610155214","volume":"15","author":"V Deshwal","year":"2022","unstructured":"Deshwal V, Yadav V, et al. Estimating COVID-19 cases using machine learning regression algorithms. Recent Adv Electr Electron Eng. 2022;15(5):390\u2013400. https:\/\/doi.org\/10.2174\/2352096515666220610155214.","journal-title":"Recent Adv Electr Electron Eng"},{"issue":"1","key":"2949_CR29","doi-asserted-by":"publisher","first-page":"18","DOI":"10.37391\/IJEER.100103","volume":"10","author":"M Rahul","year":"2022","unstructured":"Rahul M, Yadav V, et al. A new hybrid approach for efficient emotion recognition using deep learning. Int J Electr Electron Res. 2022;10(1):18\u201322. https:\/\/doi.org\/10.37391\/IJEER.100103.","journal-title":"Int J Electr Electron Res"},{"key":"2949_CR30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-33966-1_11","volume-title":"Deep learning techniques for biomedical and health informatics. Studies in BIG DATA","author":"A Kumar","year":"2020","unstructured":"Kumar A, Sarkar S, Pradhan C. Malaria disease detection using CNN technique with SGD, RMSprop and ADAM Optimizers. In: Dash S, Acharya B, Mittal M, Abraham A, Kelemen A, editors. Deep learning techniques for biomedical and health informatics. Studies in BIG DATA, vol. 68. Cham: Springer; 2020. https:\/\/doi.org\/10.1007\/978-3-030-33966-1_11."},{"key":"2949_CR31","doi-asserted-by":"publisher","first-page":"16591","DOI":"10.1007\/s11042-022-13820-0","volume":"82","author":"E Hassan","year":"2023","unstructured":"Hassan E, Shams MY, Hikal NA, et al. The effect of choosing optimizer algorithms to improve computer vision tasks: a comparative study. Multimed Tools Appl. 2023;82:16591\u2013633. https:\/\/doi.org\/10.1007\/s11042-022-13820-0.","journal-title":"Multimed Tools Appl"},{"key":"2949_CR32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12864-019-6419-1","volume":"21","author":"D Chicco","year":"2020","unstructured":"Chicco D, Jurman G. The advantages of the Matthews correlation coefficient (MCC) over F1-score and accuracy in binary classification evaluation. ChiccoJurman BMC Genom. 2020;21:1\u201313.","journal-title":"ChiccoJurman BMC Genom."}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-02949-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-024-02949-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-02949-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,28]],"date-time":"2024-05-28T17:43:41Z","timestamp":1716918221000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-024-02949-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,28]]},"references-count":32,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["2949"],"URL":"https:\/\/doi.org\/10.1007\/s42979-024-02949-4","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,28]]},"assertion":[{"value":"28 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 May 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest"}}],"article-number":"591"}}