A deep learning framework for arsenic skin disease detection leveraging dual-teacher knowledge distillation with a depthwise-separable convolution and KAN-based lightweight student model

Abstract

Arsenic poisoning in groundwater poses a major public health issue in Bangladesh. Chronic arsenic poisoning often leads to arsenicosis, a chronic disease characterized by cutaneous effects, including melanosis, leukocomelanosis and keratosis. Timely treatment of these lesions on the skin is important since prompt diagnosis and timely action are taken within the medical profession. However, this is challenging in rural areas. In most areas, there are no trained dermatologists, diagnoses tend to be subjective and there may be inadequate healthcare facilities. The proposed study involves the development of an explainable and lightweight deep learning framework for the automatic identification of skin diseases caused by arsenic, which will be computationally efficient and interpretable on a clinical scale. This system uses the dual-teacher knowledge distillation (KD) approach, in which two high-capacity models, InceptionV3 and Xception+InceptionM, are used to distill discriminative and contextual information to a small student model called Inception-Residual- KANNet (IR-KANNet). ArsenicSkinImageBD dataset was trained and validated using a stratified data split and 5-fold cross-validation to ensure class balance and model stability. The final evaluation found accuracy 0.9692, precision 0.9610, recall 0.9867 and F1-score 0.9737 on both infected and not-infected cases using stratified data split and a factor of 78.39% reduction in parameters over the teacher 1 and 35.29% in teacher 2 networks. These results indicate the appropriateness of the model for use in low-resource healthcare settings by providing convenient and reliable AI-based screening for arsenicosis detection. Grad-CAM++ and LIME make the model explainable and the resulting transparent heatmaps are consistent with the clinical regions in terms of lesions. This study is relevant for developing interpretable, efficient and domain-flexible medical AI models. It also provides a basis for further study of explainable knowledge distillation and edge-deployable solutions for the diagnosis of other dermatological disease