Skin Disease Prediction Using Efficient-Net with Integrated Clinical Decision Support System

In this follow-up study, we present an enhanced skin disease classification model using a pretrained EfficientNet backbone and a novel integrated remedy suggestion module. Building on our earlier CNN-based approach, we employ EfficientNetB3 (ImageNet-pretrained) with comprehensive data preprocessing and augmentation to improve diagnostic performance. Model architecture includes a 300×300 input pipeline, batch normalization, dense layers, and dropout for robust learning. We trained and evaluated the model on the HAM10000 dataset for both 5-epoch and 10-epoch regimes, reporting detailed metrics (accuracy, AUC, recall, loss) and demonstrating substantial gains over the previous custom CNN baseline. Importantly, we integrated a clinical knowledge module: once a lesion is classified, the system returns recommended remedies, medications, and preventive advice for that diagnosis. This combination of AI diagnosis and actionable treatment guidance exemplifies "augmented intelligence" in dermatology, aiming to support clinicians and patients with timely, explainable recommendations.