A new case based reasoning diagnosis approach within a possibilistic framework

Solar exposure behavior has led to a significant increase in melanoma cancer cases in recent years. The mortality rates caused by this disease are the highest among dermatological cancers. This is due to the complexity of diagnosis, doctors use the Case Based Reasoning approach to leverage similarity with previously diagnosed cases. While the CBR approach can effectively address complex cases, its reliance on potentially irrelevant features can limit its accuracy and introduce diagnostic errors. In this study, we introduce a novel approach that integrates CBR within a possibility theory framework to assist experts in melanoma early detection. To address the issue of non-informative features, we introduce a possibilistic selection block within our approach. This block enables the CBR system to focus solely on relevant features, thereby enhancing its accuracy. In this approach, possibility theory is intended to address the problem of ambiguity and uncertainty affecting skin images. Then, the most relevant features are selected based on a possibilistic formalism and used as key features in the similarity measure within the CBR approach. Experimental validation on two sets of optical and dermoscopic lesion image datasets illustrates that our approach can be appropriate for lesion severity classification. It achieves a specificity of 100% and an accuracy of 95% on both databases, surpassing recent existing methods in melanoma diagnosis.