AI-Enhanced System for Diabetic Foot Ulcer Localization and Classification Using Multi-Scale Neural CNN Model

Diabetic foot ulcers (DFUs), which heal slowly because of inadequate blood supply, are among the serious infections and chronic foot problems that can result from diabetes mellitus (DM). The primary consequence of DFU, which can result in amputation if left untreated.

The paper proposes an intelligent and automated approach for classifying foot images as either healthy or DFU images. At the initial stage, the proposed system presents a novel dataset containing 5500 foot images collected from diverse individuals with healthy and DFU conditions. The preprocessing stage involves three steps: the Region of Interest (ROI) method removes unnecessary portions of the foot images, the RGB images are converted to grayscale, and Non-Local Means (NLM) filtering is applied to reduce noise and remove unwanted information. Two neural feature extractors, ResNet50 and Faster R-CNN, are used to independently extract features from the foot images, and the resulting feature vectors are integrated into a single fused feature. The softmax function of Faster RCNN method classifies DFU or normal image using fused features, and bounding box method regressor function localize the ulcer region from DFU image.

Compared with relevant state-of-the-art methods, the proposed Faster RCNN-based deep learning approach with feature fusion demonstrates superior performance in DFU recognition, achieving a testing accuracy of 99.85%, specificity of 99.37%, and precision of 99.50%. With a detection accuracy of 98.83%, this fusion-based approach demonstrated competitive performance through the use of a generalization validation mechanism.

For automated diabetic foot ulcer identification and localization, the suggested fusion-based Faster R-CNN framework shows very accurate and dependable performance. These results point to its significant potential as a helpful clinical decision-making tool for the early identification and treatment of DFU.