{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:45:14Z","timestamp":1778201114620,"version":"3.51.4"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1007\/s00521-025-11809-y","type":"journal-article","created":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T05:35:39Z","timestamp":1771306539000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A comparative analysis for skin cancer detection by using explainable deep learning"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4179-3188","authenticated-orcid":false,"given":"Havva Hazel","family":"Aras","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8267-8469","authenticated-orcid":false,"given":"Nurettin","family":"Do\u011fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,17]]},"reference":[{"issue":"2","key":"11809_CR1","first-page":"95","volume":"43","author":"A Uysal","year":"2004","unstructured":"Uysal A, \u00d6zsoy SA, Erg\u00fcl \u015e (2004) \u00d6\u011frencilerin cilt kanseri risklerinin ve g\u00fcne\u015f i\u015finlarindan korunmaya y\u00f6nelik uygulamalarinin de\u011ferlendirilmesi. Ege Tip Derg 43(2):95\u201399","journal-title":"Ege Tip Derg"},{"key":"11809_CR2","doi-asserted-by":"crossref","unstructured":"Akyel C, Arici N (2022) Cilt Kanseri G\u00f6r\u00fcnt\u00fclerinde FCN8-ResNetC ve G\u00f6r\u00fcnt\u00fc \u0130\u015fleme ile K\u0131l Temizli\u011fi ve Lezyon B\u00f6l\u00fctleme. Int J Inf Technol. 15(2)","DOI":"10.17671\/gazibtd.1060330"},{"issue":"2","key":"11809_CR3","doi-asserted-by":"publisher","first-page":"111","DOI":"10.15321\/GenelTipDer.2021.295","volume":"31","author":"H Alp","year":"2021","unstructured":"Alp H, Tutun H, Alt\u0131nta\u015f L, Kaplan HM, \u015eingirik E, Kahraman \u0130 (2021) Metforminin Melanom Cilt Kanseri H\u00fccreleri \u00dczerindeki Etkisinin Ara\u015ft\u0131r\u0131lmas\u0131. Genel Tip Derg 31(2):111\u2013115","journal-title":"Genel Tip Derg"},{"key":"11809_CR4","unstructured":"Uslu M, Karaman G, \u015eavk E, \u015eendur N (2006) Adnan Menderes \u00dcniversitesi hekimlerinin deri kanserleri ve g\u00fcne\u015fin etkileri konusundaki bilgi d\u00fczeyleri ile g\u00fcne\u015ften korunma davran\u0131\u015flar\u0131n\u0131n de\u011ferlendirilmesi"},{"issue":"1","key":"11809_CR5","first-page":"52","volume":"3","author":"S \u00d6ncel","year":"2017","unstructured":"\u00d6ncel S, G\u00fcndo\u011fdu D (2017) Deri Kanseri Risk Alg\u0131s\u0131n\u0131n G\u00fcne\u015ften Korunma Davran\u0131\u015flar\u0131na Etkisi: Sistematik Derleme. Turkiye Klinikleri Public Health Nursing-Special Topics 3(1):52\u201360","journal-title":"Turkiye Klinikleri Public Health Nursing-Special Topics"},{"key":"11809_CR6","doi-asserted-by":"crossref","unstructured":"Nahata H, Singh SP (2020) Deep learning solutions for skin cancer detection and diagnosis. In: Machine learning with health care perspective: machine learning and healthcare. pp 159\u2013182","DOI":"10.1007\/978-3-030-40850-3_8"},{"issue":"1","key":"11809_CR7","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1007\/s13721-023-00437-y","volume":"13","author":"S Roy","year":"2023","unstructured":"Roy S, Pal D, Meena T (2023) Explainable artificial intelligence to increase transparency for revolutionizing healthcare ecosystem and the road ahead. Netw Model Anal Health Inform Bioinform 13(1):4","journal-title":"Netw Model Anal Health Inform Bioinform"},{"key":"11809_CR8","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.ejca.2022.02.025","volume":"167","author":"K Hauser","year":"2022","unstructured":"Hauser K et al (2022) Explainable artificial intelligence in skin cancer recognition: a systematic review. Eur J Cancer 167:54\u201369","journal-title":"Eur J Cancer"},{"issue":"11","key":"11809_CR9","first-page":"42","volume":"8","author":"I Rahman","year":"2023","unstructured":"Rahman I (2023) AI-powered personalized treatment recommendation framework for improved healthcare outcomes. J Comput Soc Dyn 8(11):42\u201351","journal-title":"J Comput Soc Dyn"},{"key":"11809_CR10","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2020.00233","volume":"7","author":"TB Jutzi","year":"2020","unstructured":"Jutzi TB et al (2020) Artificial intelligence in skin cancer diagnostics: the patients\u2019 perspective. Front Med (Lausanne) 7:233","journal-title":"Front Med (Lausanne)"},{"key":"11809_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2019.101756","volume":"102","author":"N Zhang","year":"2020","unstructured":"Zhang N, Cai Y-X, Wang Y-Y, Tian Y-T, Wang X-L, Badami B (2020) Skin cancer diagnosis based on optimized convolutional neural network. Artif Intell Med 102:101756","journal-title":"Artif Intell Med"},{"key":"11809_CR12","unstructured":"Alezab\u0131 AMM (2021) G\u00f6r\u00fcnt\u00fc i\u015fleme teknikleriyle melanom cilt kanseri b\u00f6l\u00fctlemesi. Kastamonu \u00fcniversitesi"},{"issue":"4","key":"11809_CR13","doi-asserted-by":"publisher","first-page":"1850","DOI":"10.3390\/app12041850","volume":"12","author":"L Rukhsar","year":"2022","unstructured":"Rukhsar L, Bangyal WH, Ali Khan MS, Ag Ibrahim AA, Nisar K, Rawat DB (2022) Analyzing RNA-seq gene expression data using deep learning approaches for cancer classification. Appl Sci 12(4):1850","journal-title":"Appl Sci"},{"key":"11809_CR14","doi-asserted-by":"crossref","unstructured":"Hussain S, Songhua X, Aslam MU, Waqas M, Hussain F (2024) Hypergraph convolutional neural networks for clinical diagnosis of monkeypox infections using skin virological images. Appl Soft Comput 112673","DOI":"10.1016\/j.asoc.2024.112673"},{"key":"11809_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2024.111624","volume":"159","author":"KM Hosny","year":"2024","unstructured":"Hosny KM, Said W, Elmezain M, Kassem MA (2024) Explainable deep inherent learning for multi-classes skin lesion classification. Appl Soft Comput 159:111624","journal-title":"Appl Soft Comput"},{"key":"11809_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.107449","volume":"103","author":"H Ghazouani","year":"2025","unstructured":"Ghazouani H (2025) Multi-residual attention network for skin lesion classification. Biomed Signal Process Control 103:107449","journal-title":"Biomed Signal Process Control"},{"issue":"1","key":"11809_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2018.161","volume":"5","author":"P Tschandl","year":"2018","unstructured":"Tschandl P, Rosendahl C, Kittler H (2018) The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci Data 5(1):1\u20139","journal-title":"Sci Data"},{"key":"11809_CR18","unstructured":"Howard AG (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861."},{"issue":"12","key":"11809_CR19","doi-asserted-by":"publisher","first-page":"4427","DOI":"10.3390\/s22124427","volume":"22","author":"F Zhu","year":"2022","unstructured":"Zhu F, Liu C, Yang J, Wang S (2022) An improved mobileNet network with wavelet energy and global average pooling for rotating machinery fault diagnosis. Sensors 22(12):4427","journal-title":"Sensors"},{"key":"11809_CR20","doi-asserted-by":"crossref","unstructured":"Albelwi SA (2022) Deep architecture based on DenseNet-121 model for weather image recognition. Int J Adv Comput Sci Appl 13(10)","DOI":"10.14569\/IJACSA.2022.0131065"},{"key":"11809_CR21","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":"11809_CR22","doi-asserted-by":"crossref","unstructured":"Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251\u20131258","DOI":"10.1109\/CVPR.2017.195"},{"issue":"8","key":"11809_CR23","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0249278","volume":"16","author":"W Muhammad","year":"2021","unstructured":"Muhammad W, Aramvith S, Onoye T (2021) Multi-scale Xception based depthwise separable convolution for single image super-resolution. PLoS ONE 16(8):e0249278","journal-title":"PLoS ONE"},{"key":"11809_CR24","unstructured":"Muthu Subathra L, Krishnaveni M, Paul Keins B, Joshua Vimal Raj R (2023) Breast cancer detection with Resnet50, inception V3, and Xception architecture. J Pharm Negat Results 60\u201368"},{"key":"11809_CR25","unstructured":"Selvaraju RR, Das A, Vedantam R, Cogswell M, Parikh D, Batra D (2016) Grad-CAM: Why did you say that?. arXiv preprint arXiv:1611.07450"},{"key":"11809_CR26","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision. pp 618\u2013626.","DOI":"10.1109\/ICCV.2017.74"},{"key":"11809_CR27","doi-asserted-by":"crossref","unstructured":"Ribeiro MT, Singh S, Guestrin C (2016) Why should i trust you? \u201cExplaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1135\u20131144","DOI":"10.1145\/2939672.2939778"},{"key":"11809_CR28","doi-asserted-by":"publisher","first-page":"40","DOI":"10.3758\/BF03213026","volume":"9","author":"JT Townsend","year":"1971","unstructured":"Townsend JT (1971) Theoretical analysis of an alphabetic confusion matrix. Percept Psychophys 9:40\u201350","journal-title":"Percept Psychophys"},{"key":"11809_CR29","unstructured":"Yasin ET, Koklu M (2023) Classification of organic and recyclable waste based on feature extraction and machine learning algorithms. In: Proceedings of the international conference on intelligent systems and new applications (ICISNA\u201923)"},{"key":"11809_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105507","volume":"174","author":"M Koklu","year":"2020","unstructured":"Koklu M, Ozkan IA (2020) Multiclass classification of dry beans using computer vision and machine learning techniques. Comput Electron Agric 174:105507","journal-title":"Comput Electron Agric"},{"issue":"3","key":"11809_CR31","doi-asserted-by":"publisher","first-page":"1579","DOI":"10.1007\/s10694-021-01208-9","volume":"58","author":"YS Taspinar","year":"2022","unstructured":"Taspinar YS, Koklu M, Altin M (2022) Acoustic-driven airflow flame extinguishing system design and analysis of capabilities of low frequency in different fuels. Fire Technol 58(3):1579\u20131597","journal-title":"Fire Technol"},{"key":"11809_CR32","doi-asserted-by":"crossref","unstructured":"Elham Tahsin Yasin MK (2024) A comparative analysis of machine learning algorithms for waste classification: Inceptionv3 and chi-square features. Int J Environ Sci Technol","DOI":"10.1007\/s13762-024-06233-z"},{"issue":"1","key":"11809_CR33","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1186\/s43067-025-00243-8","volume":"12","author":"AM Sarhan","year":"2025","unstructured":"Sarhan AM et al (2025) Achieving high-accuracy skin cancer classification with deep learning optimized by ant colony algorithm. J Electr Syst Inf Technol 12(1):49","journal-title":"J Electr Syst Inf Technol"},{"issue":"1","key":"11809_CR34","doi-asserted-by":"publisher","first-page":"4938","DOI":"10.1038\/s41598-025-89230-7","volume":"15","author":"B Ozdemir","year":"2025","unstructured":"Ozdemir B, Pacal I (2025) A robust deep learning framework for multiclass skin cancer classification. Sci Rep 15(1):4938","journal-title":"Sci Rep"},{"issue":"1","key":"11809_CR35","doi-asserted-by":"publisher","first-page":"14913","DOI":"10.1038\/s41598-025-98205-7","volume":"15","author":"UK Lilhore","year":"2025","unstructured":"Lilhore UK et al (2025) SkinEHDLF a hybrid deep learning approach for accurate skin cancer classification in complex systems. Sci Rep 15(1):14913","journal-title":"Sci Rep"},{"key":"11809_CR36","doi-asserted-by":"crossref","unstructured":"Murmu A, Swati S, Kumar N, Murmu R, Dash Y (2024) Deep learning based encoder-decoder model for skin cancer classification. In 2024 7th international conference on contemporary computing and informatics (IC3I), vol 7, IEEE, pp 1454\u20131459","DOI":"10.1109\/IC3I61595.2024.10829364"},{"issue":"5","key":"11809_CR37","doi-asserted-by":"publisher","DOI":"10.3390\/app12052677","volume":"12","author":"L Hoang","year":"2022","unstructured":"Hoang L, Lee S-H, Lee E-J, Kwon K-R (2022) Multiclass skin lesion classification using a novel lightweight deep learning framework for smart healthcare. Appl Sci 12(5):2677","journal-title":"Appl Sci"},{"key":"11809_CR38","doi-asserted-by":"crossref","unstructured":"Adebiyi A et al (2024) Accurate skin lesion classification using multimodal learning on the ham10000 dataset. MedRxiv, p 2024.05. 30.24308213","DOI":"10.1101\/2024.05.30.24308213"},{"key":"11809_CR39","doi-asserted-by":"crossref","unstructured":"Garg R, Maheshwari S, Shukla A (2020) Decision support system for detection and classification of skin cancer using CNN. In: Innovations in computational intelligence and computer vision: proceedings of ICICV 2020, Springer, pp 578\u2013586","DOI":"10.1007\/978-981-15-6067-5_65"},{"key":"11809_CR40","doi-asserted-by":"crossref","unstructured":"Sae-Lim W, Wettayaprasit W, Aiyarak P (2019) Convolutional neural networks using MobileNet for skin lesion classification. In: 2019 16th international joint conference on computer science and software engineering (JCSSE), IEEE, pp 242\u2013247","DOI":"10.1109\/JCSSE.2019.8864155"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11809-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11809-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11809-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T03:43:19Z","timestamp":1773718999000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11809-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":40,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["11809"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11809-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2]]},"assertion":[{"value":"22 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2026","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 did not receive support from any organization for the submitted work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"50"}}