Advanced Machine Learning Techniques for Diabetic Foot Ulcer Detection

Abstract

Identifying diabetes-related foot ulcers early is a life-saving intervention since early management is essential for proper control of the disease and for the prevention of complications. In this approach, CNNs (Convolutional Neural Networks), FFNNs (Feed-Forward Neural Networks), SVCs (Support Vector Classifiers), and LR models (Logistic Regression) are trained and tested to classify diabetic foot ulcer images. This prepares the data and tweaks the model to maximize the model`s performance. Based on test accuracy and performance metrics, the models will be evaluated. The findings point to the efficiency of these deep convolutional neural network models in separating between normal and ulcer images, possibly making them suitable for clinical application in diabetic foot ulcer screening.