Pattern and texton-based feature descriptors for Diabetic foot ulcer detection

Diabetic foot ulcers (DFU) are a dangerous side effect of diabetes mellitus that significantly impairs a patient's health and standard of living. For efficient treatment and to avoid serious outcomes like infections and amputations, DFUs must be detected promptly and precisely. This paper introduces a novel Deep Learning Strategy-based framework for Diabetic Foot Ulcer Detection (DLS-DFUD). The DLS-DFUD framework involves four key steps: preprocessing, segmentation, extraction of features, and classification. The detection process starts with acquiring the input image, which is then preprocessed using Wavelet Transform-based Wiener Filtering (WT-WF) to minimize noise and improve image quality. The cleaned image is subsequently segmented with a Middle Convolutional layer Assisted U-Net (MCA-U-Net) model, precisely distinguishing DFU regions from healthy tissue. From this segmentation, various features are extracted, including Median Binary Patterns (MBP), Modified Pixel Computation in Multi-Texton (MPC-MT), shape features, and statistical features to capture crucial ulcer characteristics.