A Lightweight Explainable Hybrid Deep Learning Approach for Early Skin Disease Detection

Early detection of skin pathology is critical for patient survival and treatment effectiveness, particularly in the case of aggressive malignancies such as melanoma. In this study, we propose a lightweight and explainable hybrid deep learning approach for early identification of skin diseases. The proposed approach combines a transformer-based module to capture the global contextual dependencies with a CNN for an efficient local feature extraction. In addition, Grad-CAM approach is used to increase the interpretability of the model and offer visual explanations for the forecasts. We evaluate the suggested technique on a publically accessible dermoscopic benchmark and achieve 93% accuracy, 92% precision, 91% recall and 92% F1-score, outperforming numerous mainstream designs. The experimental results imply that the proposed approach achieves a good trade-off between accuracy, computational economy, and interpretability for real-world and resource-limited medical applications.