Advanced deep learning for early diagnosis of arsenic-induced dermatological conditions through dermoscopic image evaluation

Timely recognition of dermatological manifestations caused by toxic environmental exposure is vital for effective healthcare management. Arsenic, a widespread contaminant in groundwater, has severe dermatological effects, leading to chronic disorders that often remain undiagnosed in their early stages. This study presents an advanced deep learning framework designed to support the early diagnosis of arsenic-induced skin conditions through dermoscopic image analysis. The research utilised a comprehensive dataset of 8892 dermoscopic images collected from four field sites in Bangladesh, encompassing both arsenic-exposed and unaffected individuals. Discriminative image features were extracted using a synergistic ResNet-DenseNet architecture, which captures both local textural and global contextual representations. The extracted features were subsequently classified using the k-Nearest Neighbour algorithm to distinguish arsenic-affected from healthy skin images. The proposed method achieved 99.37% classification accuracy, a 99.36% F1-score, 99.14% sensitivity and 99.59% recall, reflecting its strong diagnostic reliability. These outstanding results suggest that the framework can efficiently assist dermatologists by providing automated, consistent and objective evaluation of arsenic-related lesions. It also provides a data-driven method for monitoring public health in areas where arsenic contamination is a long-term problem. Overall, the study demonstrates the clinical potential of deep learning-based dermoscopic analysis for improving the early detection and management of arsenic-related dermatological disorders.