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1 International Journal of Applied Mathematics and Computer Science

1 2024

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Enhancing multi-class prediction of skin lesions with feature importance assessment

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Paulauskaite-Taraseviciene A. , Sutiene K. , Dimsa N. , Valiukeviciene S.

International Journal of Applied Mathematics and Computer Science

2024

tom Vol. 34, no. 4

617--629

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.