Dense-ShuffleGCANet An Attention-Driven Deep Learning Approach for Diabetic Foot Ulcer Classification Using Refined Spatio-Dimensional Features

This project presents Dense-ShuffleGCANet, an advanced deep learning-based diagnostic framework for Diabetic Foot Ulcer (DFU) classification, leveraging both spatial and channel attention mechanisms. We utilize four high-performance architectures—DenseNet-169, ShuffleNet, DenseNet+CCDGS (Channel-wise Cross-Domain Global Spatial), and DenseNet+CCDGS+TA (Triplet Attention)—trained on the publicly available Kaggle DFU dataset, achieving classification accuracies exceeding 95%. These models effectively distinguish between normal and abnormal foot conditions by capturing refined spatio-dimensional features. To ensure practical usability, we have developed a Flask-based web application integrated with HTML, CSS, and JavaScript. Users can register, log in, upload foot images, and receive real-time classification results, enabling remote screening and early intervention. This system serves as a scalable and accessible tool for healthcare providers and patients, offering an accurate and efficient solution for automated DFU detection, ultimately contributing to improved diabetic care and ulcer prevention.

Keywords: Diabetic Foot Ulcer (DFU), Deep Learning, DenseNet-169, ShuffleNet, CCDGS, Triplet Attention, Spatio-Dimensional Features, Flask Web App, Medical Image Classification, Abnormality Detection, Remote Diagnosis, Healthcare AI.