Investigating melanoma classification in dermatoscopic images with convolutional neural networks using melanin and erythema indices

Alexey Kotlik; Nhan Do; Gil Alterovitz; Rafael Fricks

doi:10.1117/12.3006966

3 April 2024 Investigating melanoma classification in dermatoscopic images with convolutional neural networks using melanin and erythema indices

Alexey Kotlik, Nhan Do, Gil Alterovitz, Rafael Fricks

Author Affiliations +

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 129271N (2024) https://doi.org/10.1117/12.3006966
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Skin cancers, notably melanoma, pose a significant health risk, with rising incidence rates and mortality rates. Early detection through screening is crucial, and the Department of Veterans Affairs has prioritized improved melanoma screening. Teledermatology, with digital dermatoscopy, offers a promising avenue for initial lesion assessment, prompting the exploration of algorithmic screening using deep learning. This paper investigates the efficacy of incorporating melanin index (MI) and erythema index (EI) as additional features, along with the conversion of images to the HSV color space, for use with deep learning models in melanoma classification. Considering the distinct clustering of human skin color in various color spaces, our study aims to explore the advantages of alternative color representations. Building on prior work and organized into two phases, our experiments utilize a diverse set of model architectures trained on images of varying sizes. Phase 1 aligns with the 2020 SIIM-ISIC Melanoma Classification Challenge, while Phase 2 involves an expanded dataset and more robust metrics. Despite optimistic outcomes in the earlier phase, our findings reveal no significant performance improvement when incorporating MI or EI in deep learning models for melanoma detection. This study contributes valuable insights for refining deep learning approaches in dermatoscopy, offering a cautionary note on the efficacy of specific features and of color space transformation.

Conference Presentation

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Alexey Kotlik, Nhan Do, Gil Alterovitz, and Rafael Fricks "Investigating melanoma classification in dermatoscopic images with convolutional neural networks using melanin and erythema indices", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 129271N (3 April 2024); https://doi.org/10.1117/12.3006966

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