From Pixels to Diagnosis: AI-Driven Skin Lesion Recognition

Recommended citation: Monica Bianchini, Paolo Andreini, & Simone Bonechi. From Pixels to Diagnosis: AI-Driven Skin Lesion Recognition. In Advances in Smart Healthcare Paradigms and Applications: Outstanding Women in Healthcare—Volume 1 (pp. 115-135). Cham: Springer Nature Switzerland. 2023. (BibTex)

Abstract

Skin cancer is a serious public health problem with a sharply increasing incidence in recent years, which has a major impact on quality of life and can be disfiguring or even fatal. Deep learning techniques can be used to analyze dermoscopic images, resulting in automated systems that can improve the clinical confidence of the diagnosis – also avoiding unnecessary surgery – help clinicians objectively communicate its outcome, reduce errors related to human fatigue, and cut costs affecting the health system. In this chapter, we present an entire pipeline to analyze skin lesion images in order to distinguish nevi from melanomas, also integrating patient clinical data to reach a diagnosis. Furthermore, to make our artificial intelligence tool explainable for both clinicians and patients, dermoscopic images are further processed to obtain their segmented counterparts, where the lesion contour is easily observable, and saliency maps, highlighting the areas of the lesion that prompted the classifier to make its decision. Experimental results are promising and have been positively evaluated by human experts.

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