Skin Lesion Classification with EBMS Based on Energy Correlation Approach

Early detection and classification of skin lesions present a significant challenge in dermatology and healthcare, particularly in the early identification of malignant skin lesions. Traditional methods often struggle to capture the complex relationships among features in dermatological images and face difficulties in handling imbalanced datasets. We propose a novel approach that integrates Energy-Based Models (EBMs) with the Energy Correlation (EC) methodology. Experiments conducted on the ISIC 2020 dataset demonstrated the model's capability in detecting and classifying skin lesions, particularly malignant ones, achieving an accuracy of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 8, 2 3 \%}$</tex>. These results introduce a new direction for applying EBMs with Energy Correlation to other complex medical image detection and classification tasks, while also offering an effective diagnostic support tool for dermatology specialists.