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Paper details
Number 4 - December 2024
Volume 34 - 2024
Enhancing multi-class prediction of skin lesions with feature importance assessment
Agne Paulauskaite-Taraseviciene, Kristina Sutiene, Nojus Dimsa, Skaidra Valiukeviciene
Abstract
Numerous image processing techniques have been developed for the identification of various types of skin lesions. In real-world scenarios, the specific lesion type is often unknown in advance, leading to a multi-class prediction challenge. The
available evidence underscores the importance of employing a comprehensive array of diverse features and subsequently
identifying the most important ones as a crucial step in visual diagnostics. For this purpose, we addressed both binary and
five-class classification tasks using a small dataset, with skin lesions prevalent in Lithuania. The model was trained using a
rich set of 662 features, encompassing both conventional image features and graph-based ones, which were obtained from
the superpixel graph generated using Delaunay triangulation. We explored the influence of feature importance determined
by SHAP values, resulting in a weighted F1-score of 92.48% for the two-class classification and 71.21% for the five-class
prediction.
Keywords
skin lesion, feature extraction, graph theory, multi-class prediction, SHAP values