Toward Accessible Dermatology: Skin Lesion Classification Using Deep Learning Models on Mobile-Acquired Images

• URL: http://arxiv.org/abs/2509.04800v1
• Date: Fri, 05 Sep 2025 04:31:16 GMT
• Title: Toward Accessible Dermatology: Skin Lesion Classification Using Deep Learning Models on Mobile-Acquired Images
• Authors: Asif Newaz, Masum Mushfiq Ishti, A Z M Ashraful Azam, Asif Ur Rahman Adib,
• Abstract summary: In this work, we curate a large dataset of over 50 skin disease categories captured with mobile devices.<n>We evaluate multiple convolutional neural networks and Transformer-based architectures.<n>Our results underscore the potential of Transformer-based approaches for mobile-acquired skin lesion classification.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Skin diseases are among the most prevalent health concerns worldwide, yet conventional diagnostic methods are often costly, complex, and unavailable in low-resource settings. Automated classification using deep learning has emerged as a promising alternative, but existing studies are mostly limited to dermoscopic datasets and a narrow range of disease classes. In this work, we curate a large dataset of over 50 skin disease categories captured with mobile devices, making it more representative of real-world conditions. We evaluate multiple convolutional neural networks and Transformer-based architectures, demonstrating that Transformer models, particularly the Swin Transformer, achieve superior performance by effectively capturing global contextual features. To enhance interpretability, we incorporate Gradient-weighted Class Activation Mapping (Grad-CAM), which highlights clinically relevant regions and provides transparency in model predictions. Our results underscore the potential of Transformer-based approaches for mobile-acquired skin lesion classification, paving the way toward accessible AI-assisted dermatological screening and early diagnosis in resource-limited environments.

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