{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T14:55:00Z","timestamp":1785336900956,"version":"3.55.0"},"reference-count":63,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,12,22]],"date-time":"2024-12-22T00:00:00Z","timestamp":1734825600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie-Innovative Training Network 2020","award":["956394"],"award-info":[{"award-number":["956394"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Skin cancer is among the most prevalent cancers globally, emphasizing the need for early detection and accurate diagnosis to improve outcomes. Traditional diagnostic methods, based on visual examination, are subjective, time-intensive, and require specialized expertise. Current artificial intelligence (AI) approaches for skin cancer detection face challenges such as computational inefficiency, lack of interpretability, and reliance on standalone CNN architectures. To address these limitations, this study proposes a comprehensive pipeline combining transfer learning, feature selection, and machine-learning algorithms to improve detection accuracy. Multiple pretrained CNN models were evaluated, with Xception emerging as the optimal choice for its balance of computational efficiency and performance. An ablation study further validated the effectiveness of freezing task-specific layers within the Xception architecture. Feature dimensionality was optimized using Particle Swarm Optimization, reducing dimensions from 1024 to 508, significantly enhancing computational efficiency. Machine-learning classifiers, including Subspace KNN and Medium Gaussian SVM, further improved classification accuracy. Evaluated on the ISIC 2018 and HAM10000 datasets, the proposed pipeline achieved impressive accuracies of 98.5% and 86.1%, respectively. Moreover, Explainable-AI (XAI) techniques, such as Grad-CAM, LIME, and Occlusion Sensitivity, enhanced interpretability. This approach provides a robust, efficient, and interpretable solution for automated skin cancer diagnosis in clinical applications.<\/jats:p>","DOI":"10.3390\/jimaging10120332","type":"journal-article","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T08:16:07Z","timestamp":1734941767000},"page":"332","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Explainable AI-Based Skin Cancer Detection Using CNN, Particle Swarm Optimization and Machine Learning"],"prefix":"10.3390","volume":"10","author":[{"given":"Syed Adil Hussain","family":"Shah","sequence":"first","affiliation":[{"name":"Department of Research and Development (R&D), GPI SpA, 38123 Trento, Italy"},{"name":"PolitoBIOMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6010-6777","authenticated-orcid":false,"given":"Syed Taimoor Hussain","family":"Shah","sequence":"additional","affiliation":[{"name":"PolitoBIOMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roa\u2019a","family":"Khaled","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, University of C\u00e1diz, 11519 Puerto Real, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1042-8911","authenticated-orcid":false,"given":"Andrea","family":"Buccoliero","sequence":"additional","affiliation":[{"name":"Department of Research and Development (R&D), GPI SpA, 38123 Trento, Italy"},{"name":"Human Science Department, Universit\u00e0 degli studi di Verona, Lungadige Porta Vittoria, 17, 37129 Verona, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Syed Baqir Hussain","family":"Shah","sequence":"additional","affiliation":[{"name":"Department of Computer Science, COMSATS University Islamabad (CUI), Wah Campus, Wah 47000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Angelo","family":"Di Terlizzi","sequence":"additional","affiliation":[{"name":"Department of Research and Development (R&D), GPI SpA, 38123 Trento, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giacomo","family":"Di Benedetto","sequence":"additional","affiliation":[{"name":"7HC SRL, 00198 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1918-1772","authenticated-orcid":false,"given":"Marco Agostino","family":"Deriu","sequence":"additional","affiliation":[{"name":"PolitoBIOMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.clindermatol.2007.09.013","article-title":"Hyaluronan in skin: Aspects of aging and its pharmacologic modulation","volume":"26","author":"Stern","year":"2008","journal-title":"Clin. 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