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

Swin Transformer achieved the best performance with 86% accuracy. LIME and Grad-CAM visual explanations were utilized for model in-terpretability. External validation with unseen images demonstrated strong generalization across conditions. A web-based diagnostic tool was developed for arsenic screening in rural communities. 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 arseni-cosis 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 arseni-Corresponding author: Asif Newaz, Email: eee.asifnewaz@iut-dhaka.edu cosis 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. Introduction Skin diseases are among the most common health problems worldwide, impacting millions of individuals across diverse populations. Globally, skin diseases represent a massive public health burden: recent estimates indicate that in 2021 alone, approximately 4.69 billion new cases were recorded, and skin conditions remain the fourth leading cause of non-fatal disease burden and among the top causes of disability worldwide [1]. These diseases range from mild infections to chronic and life-threatening disorders.