DEEP NEURAL NETWORK-BASED APPROACH FOR DIABETIC FOOT ULCER CLASSIFICATION

Diabetic Foot Ulcer (DFU) is a severe complication of diabetes, necessitating early detection to prevent limb amputation. This study proposes a novel hybrid Convolutional Neural Network (CNN) model leveraging EfficientNetV2S architecture for automated DFU classification. Utilizing a dataset of 1,055 foot skin patches (512 abnormal, 543 normal), the model integrates transfer learning, dropout layers, global average pooling, and cosine decay learning rate optimization. Pre-trained weights from EfficientNetV2S were fine-tuned, achieving a state-of-the-art accuracy of 99.94% on the test set. Training and validation curves demonstrated stable convergence, with minimal overfitting, underscoring the model’s robustness. Comparative analysis with existing methods highlights superior performance, suggesting its potential for clinical deployment in early DFU diagnosis. This approach enhances diagnostic precision, enabling timely intervention to improve patient outcomes.