Deep learning has become a vital tool in medical image analysis, particularly for dermatology, where early detection of skin diseases is critical. This study presents an optimized system for automatic classification of skin conditions using dermoscopic images. A MobileNetV2-based convolutional neural network (CNN) was fine-tuned with transfer learning to enhance performance across multiple skin disease categories. Images were preprocessed through resizing and normalization before classification. To improve interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) was integrated to visualize discriminative regions. The system was evaluated using accuracy, sensitivity, specificity, precision, recall, F1-score, and AUC-ROC. Results demonstrate promising accuracy but reveal limited sensitivity for certain classes, reflecting challenges of dataset imbalance and visual similarity across conditions. The model’s deployment through a Streamlit-based interface enables real-time predictions and interactive visualization, offering potential for use as a screening tool in resource-constrained settings. Future work should emphasize validation on larger, diverse datasets and explore advanced augmentation strategies to enhance generalization.
Makolo et al. (Wed,) studied this question.