This approach could help dermatologists in their clinical practice for patients with atypical and difficult-to-diagnose pigmented lesions. These results suggest that our framework provides an effective and explainable decision-making strategy. Furthermore, our approach outperformed existing methods in this task, improving the balanced accuracy (BACC) of the best compared method from 77% to 86%. 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. 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).

Then, a subset of players has the strategy to refine the diagnosis for difficult lesions with medium and uncertain prediction. The players’ 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). The proposed framework includes a multi-class CNN and six binary CNNs assimilated to players. Pursuing the goal of improving the performance of existing systems, our approach also attempts to bring more transparency in the decision process. This study presents a new framework for automated melanoma diagnosis. Decision theory, especially game theory, is a potential solution as it focuses on identifying the best decision option that maximizes the decision-maker’s expected utility. This may be explained by the dermatologist’s fear of being misled by a false negative and the assimilation of CNNs to a “black box”, making their decision process difficult to understand by a non-expert. 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.

Thus, AI-assisted diagnosis is considered as a possible solution for this issue. Early detection of melanoma remains a daily challenge due to the increasing number of cases and the lack of dermatologists.

Descargar gratis rapid php full version