An Integrated Design for Classification and Localization of Diabetic Foot Ulcer based on CNN and YOLOv2-DFU Models

Abstract

Diabetes is a chronic disease, if not treated in time may lead to many complications including diabetic foot ulcers (DFU). DFU is a dangerous disease, it needs regular treatment otherwise it may lead towards foot amputation. The DFU is classified into two categories such as infection (bacteria) and the ischaemia (inadequate supply of the blood). The DFU detection at an initial phase is a tough procedure. Therefore in this research work 16 layers convolutional neural network (CNN) for example 01 input, 03 convolutional, 03 batch-normalization, 01 average pooling, 01 skips convolutional, 03 ReLU, 01 add (element-wise addition of two inputs), fully connected, softmax and classification output layers for classification and YOLOv2-DFU for localization of infection/ischaemia models are proposed. In the classification phase, deep features are extracted and supplied to the number of classifiers such as KNN, DT, Ensemble, softmax, and NB to analyze the classification results for the selection of best classifiers. After the experimentation, we observed that DT and softmax achieved consistent results for the detection of ischaemia/infection in all performance metrics such as sensitivity, specificity, and accuracy as compared with other classifiers. In addition, after the classification, the Gradient-weighted class activation mapping (Grad-Cam) model is used to visualize the high-level features of the infected region for better understanding. The classified images are passed to the YOLOv2-DFU network for infected region localization. The Shuffle network is utilized as a mainstay of the YOLOv2 model in which bottleneck extracted features through ReLU node-199 layer and passed to the YOLOv2 model. The proposed method is validated on the newly developed DFU-Part (B) dataset and the results are compared with the latest published work using the same dataset.