An End-to-End Deep Learning Framework for Arsenicosis Diagnosis Using Mobile-Captured Skin Images

• URL: http://arxiv.org/abs/2509.08780v1
• Date: Wed, 10 Sep 2025 17:08:31 GMT
• Title: An End-to-End Deep Learning Framework for Arsenicosis Diagnosis Using Mobile-Captured Skin Images
• Authors: Asif Newaz, Asif Ur Rahman Adib, Rajit Sahil, Mashfique Mehzad,
• Abstract summary: Arsenicosis is a serious public health concern in South and Southeast Asia.<n>Its early cutaneous manifestations are clinically significant but often underdiagnosed.<n>We propose an end-to-end framework for arsenicosis diagnosis using mobile phone-captured skin images.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Background: Arsenicosis is a serious public health concern in South and Southeast Asia, primarily caused by long-term consumption of arsenic-contaminated water. Its early cutaneous manifestations are clinically significant but often underdiagnosed, particularly in rural areas with limited access to dermatologists. Automated, image-based diagnostic solutions can support early detection and timely interventions. Methods: In this study, we propose an end-to-end framework for arsenicosis diagnosis using mobile phone-captured skin images. A dataset comprising 20 classes and over 11000 images of arsenic-induced and other dermatological conditions was curated. Multiple deep learning architectures, including convolutional neural networks (CNNs) and Transformer-based models, were benchmarked for arsenicosis detection. Model interpretability was integrated via LIME and Grad-CAM, while deployment feasibility was demonstrated through a web-based diagnostic tool. Results: Transformer-based models significantly outperformed CNNs, with the Swin Transformer achieving the best results (86\\% accuracy). LIME and Grad-CAM visualizations confirmed that the models attended to lesion-relevant regions, increasing clinical transparency and aiding in error analysis. The framework also demonstrated strong performance on external validation samples, confirming its ability to generalize beyond the curated dataset. Conclusion: The proposed framework demonstrates the potential of deep learning for non-invasive, accessible, and explainable diagnosis of arsenicosis from mobile-acquired images. By enabling reliable image-based screening, it can serve as a practical diagnostic aid in rural and resource-limited communities, where access to dermatologists is scarce, thereby supporting early detection and timely intervention.

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