Attention-Based Deep Feature Aggregation Network for Skin Lesion Classification

Early and accurate detection of dermatological conditions, particularly melanoma, is critical for effective treatment and improved patient outcomes. Misclassifications may lead to delayed diagnosis, disease progression, and severe complications in medical image processing. Hence, robust and reliable classification techniques are essential to enhance diagnostic precision in clinical practice. This study presents a deep learning-based framework designed to improve feature representation while maintaining computational efficiency. The proposed architecture integrates multi-level feature aggregation with a squeeze-and-excitation attention mechanism to effectively extract salient patterns from dermoscopic medical images. The model is rigorously evaluated on five publicly available benchmark datasets—ISIC-2019, ISIC-2020, SKINL2, MED-NODE, and HAM10000—covering a diverse spectrum of dermatological medical disorders. Experimental results demonstrate that the proposed method consistently outperforms existing approaches in classification performance, achieving accuracy rates of 94.41% and 97.45% on the MED-NODE and HAM10000 datasets, respectively. These results underscore the method’s potential for real-world deployment in automated skin lesion analysis and clinical decision support.