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

Abstract: 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.

Mohammad Barr. “A Lightweight Explainable Hybrid Deep Learning Approach for Early Skin Disease Detection”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.5 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170580

@article{Barr2026,
title = {A Lightweight Explainable Hybrid Deep Learning Approach for Early Skin Disease Detection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170580},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170580},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {5},
author = {Mohammad Barr}
}