{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T20:51:12Z","timestamp":1790023872363,"version":"4.0.1"},"reference-count":59,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T00:00:00Z","timestamp":1668038400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001665","name":"French National Research Agency","doi-asserted-by":"publisher","award":["ANR-20-CE45-0026"],"award-info":[{"award-number":["ANR-20-CE45-0026"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Early detection of melanoma remains a daily challenge due to the increasing number of cases and the lack of dermatologists. Thus, AI-assisted diagnosis is considered as a possible solution for this issue. Despite the great advances brought by deep learning and especially convolutional neural networks (CNNs), computer-aided diagnosis (CAD) systems are still not used in clinical practice. This may be explained by the dermatologist\u2019s fear of being misled by a false negative and the assimilation of CNNs to a \u201cblack box\u201d, making their decision process difficult to understand by a non-expert. Decision theory, especially game theory, is a potential solution as it focuses on identifying the best decision option that maximizes the decision-maker\u2019s expected utility. This study presents a new framework for automated melanoma diagnosis. Pursuing the goal of improving the performance of existing systems, our approach also attempts to bring more transparency in the decision process. The proposed framework includes a multi-class CNN and six binary CNNs assimilated to players. The players\u2019 strategies is to first cluster the pigmented lesions (melanoma, nevus, and benign keratosis), using the introduced method of evaluating the confidence of the predictions, into confidence level (confident, medium, uncertain). Then, a subset of players has the strategy to refine the diagnosis for difficult lesions with medium and uncertain prediction. We used EfficientNetB5 as the backbone of our networks and evaluated our approach on the public ISIC dataset consisting of 8917 lesions: melanoma (1113), nevi (6705) and benign keratosis (1099). The proposed framework achieved an area under the receiver operating curve (AUROC) of 0.93 for melanoma, 0.96 for nevus and 0.97 for benign keratosis. Furthermore, our approach outperformed existing methods in this task, improving the balanced accuracy (BACC) of the best compared method from 77% to 86%. These results suggest that our framework provides an effective and explainable decision-making strategy. This approach could help dermatologists in their clinical practice for patients with atypical and difficult-to-diagnose pigmented lesions. We also believe that our system could serve as a didactic tool for less experienced dermatologists.<\/jats:p>","DOI":"10.3390\/ijms232213838","type":"journal-article","created":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T19:16:23Z","timestamp":1668107783000},"page":"13838","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Computer Aided Diagnosis of Melanoma Using Deep Neural Networks and Game Theory: Application on Dermoscopic Images of Skin Lesions"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3897-5693","authenticated-orcid":false,"given":"Arthur Cartel","family":"Foahom Gouabou","sequence":"first","affiliation":[{"name":"LIS, CNRS, Aix Marseille University, 13288 Marseille, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7540-0610","authenticated-orcid":false,"given":"Jules","family":"Collenne","sequence":"additional","affiliation":[{"name":"LIS, CNRS, Aix Marseille University, 13288 Marseille, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8747-5011","authenticated-orcid":false,"given":"Jilliana","family":"Monnier","sequence":"additional","affiliation":[{"name":"LIS, CNRS, Aix Marseille University, 13288 Marseille, France"},{"name":"Research Cancer Centre of Marseille, Inserm, CNRS, Aix-Marseille University, 13273 Marseille, France"},{"name":"Dermatology and Skin Cancer Department, La Timone Hospital, AP-HM, Aix-Marseille University, 13385 Marseille, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1728-3532","authenticated-orcid":false,"given":"Rabah","family":"Iguernaissi","sequence":"additional","affiliation":[{"name":"LIS, CNRS, Aix Marseille University, 13288 Marseille, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean-Luc","family":"Damoiseaux","sequence":"additional","affiliation":[{"name":"LIS, CNRS, Aix Marseille University, 13288 Marseille, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdellatif","family":"Moudafi","sequence":"additional","affiliation":[{"name":"LIS, CNRS, Aix Marseille University, 13288 Marseille, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9233-0020","authenticated-orcid":false,"given":"Djamal","family":"Merad","sequence":"additional","affiliation":[{"name":"LIS, CNRS, Aix Marseille University, 13288 Marseille, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1229","DOI":"10.1038\/s41591-020-0942-0","article-title":"Human\u2013computer collaboration for skin cancer recognition","volume":"26","author":"Tschandl","year":"2020","journal-title":"Nat. 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