Explainable deep learning for skin cancer detection using swish-activated convolutional networks
Rattachement africain : in, Éthiopie. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Skin cancer is among the most frequent and fatal illnesses in the world, but early and correct diagnosis is one of the main challenges because of the complicated visual patterns of skin lesions and the absence of interpretable diagnostic devices. Conventional techniques are largely based on experienced dermatologists, but manual inspection is time-consuming, subjective, and liable to misinterpretation, with high false positive outputs. To address these issues, we present a unique DCNN architecture that employs the Swish activation function to effectively identify complex patterns in the skin lesion dataset. This model has remarkable performance in diagnosing skin cancer, as evidenced by a 98.31% accuracy rate, a 98.12% precision rate, a 98.01% recall rate, and an F1-score of 98.09%. Utilising several localised and global explainable artificial intelligence (XAI) approaches, we evaluate the model's predictions to ensure transparency and reliability in medical contexts. To reconcile the disparity between AI research and its use in healthcare, our findings underscore the necessity of integrating deep learning with explainability. These XAI solutions tackle critical concerns such as inclusiveness, transparency, and error control, providing medical practitioners with a comprehensible and reliable framework for assessing the model's reasoning process. The suggested technique establishes a reliable mechanism to assist physicians in the early and precise identification of skin cancer, while enhancing diagnostic accuracy. Future studies will focus on enhancing the model's computational efficiency and incorporating more datasets to ensure its durability and fairness across various demographic groupings.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Explainable deep learning for skin cancer detection using swish-activated convolutional networks
- Date Crossref
- 10/01/2026
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Kazi Nazrul University pays non établi dans la noticeUniversité ou école supérieure
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Siksha O Anusandhan University pays non établi dans la noticeUniversité ou école supérieure
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Symbiosis International University Symbiosis Institute of Technology pays non établi dans la noticeUniversité ou école supérieure
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Bule Hora University Éthiopie (code pays fourni par la source)Université ou école supérieure
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Asansol Engineering College Department of Artificial Intelligence & Machine Learning pays non établi dans la noticeUniversité ou école supérieure
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Sri Venkateswara College of Engineering & Technology (Autonomous) Department of Computer Science and Engineering pays non établi dans la noticeUniversité ou école supérieure
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Kalaignarkarunanidhi Institute of Technology Department of Artificial Intelligence & Data Science pays non établi dans la noticeStructure de recherche
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Siksha 'O' Anusandhan (Deemed to University) Department of CSE pays non établi dans la noticeUniversité ou école supérieure
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College of Business and Economics Department of Management Éthiopie (pays nommé en fin d’affiliation)Université ou école supérieure
Kazi Nazrul University, Siksha O Anusandhan University et Symbiosis Institute of Technology — Symbiosis International University, avec 6 autres affiliations. Pays d’affiliation : Éthiopie.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.