AN Advanced AI Approach-Based Skin Disease Prediction System Utilizing EN-QNN and Grad-CAM in IoMT Environment

Skin acts as a natural shield, protecting the body from ultraviolet rays, extreme weather, and harmful chemicals. However, it can be affected by pollution, weakened immunity, and unhealthy lifestyles, leading to various skin diseases. Early detection of these conditions is crucial for timely treatment and better outcomes. Existing research has often overlooked skin diseases with similar visual characteristics, making it challenging to distinguish between different conditions using visual inspection alone. To address this, the paper proposes an AI-enabled prediction framework for skin disease prediction using EN-QNN and Grad-CAM. Initially, the images are collected using IoMT devices of skin diseases and undergo preprocessing, which includes resizing, noise removal and contrast enhancement using AK-CLAHE, followed by color analysis and segmentation using YCbCr and DF-U-Net. Morphological operations are then applied during post-processing. The shape and structure of lesions are analyzed using CMED. Meanwhile, using Grad-CAM, Contextual Information Analysis (CIA) is performed on preprocessed data. Concurrently, disease symptom prediction data (i.e. clinical data) are collected, and features are extracted from this data, including boundary localization and CIA. Finally, skin diseases are classified using EN-QNN. The proposed model achieved a high accuracy of 98.68051%, surpassing current techniques.