NFU-Net: An Automated Framework for the Detection of Neurotrophic Foot Ulcer Using Deep Convolutional Neural Network

Neurotrophic Foot Ulcer (NFU) is most common in patients with diabetes mellitus, and it may result in amputation of the lower extremity (leg and foot). Current methods used for NFU diagnosis are highly complex, report lesser accuracy, and require more computation time and specialists. The main objective of this present work is to design and develop a novel lightweight convolutional neural network (CNN) model to diagnose NFU called NFU-Net. This work utilizes a Diabetic Foot Ulcer (DFU) dataset, and it consists of 1038 abnormal and 641 normal images and naturally transformed technique is used to transfer the data using mathematical operation. We have designed a 22-layers customized CNN with parallel filter architecture to extract highly discriminative features (inter-class and intraclass) from the images to diagnose NFU with two different activation functions, namely Rectified Linear Unit (ReLU) and parametric Rectified Linear Unit (PReLU). The performance of the NFU-Net is compared with the State-Of-The-Art (SOTA) CNN's such as Alex Net, LeNet, DFU-Net, and DFU QUT-Net based on six performance measures (accuracy, precision, sensitivity, specificity, F1 score, Matthews's Correlation Coefficient (MCC) on both original and augmented datasets. The proposed NFU-Net reports an accuracy of nearly 2.5% to 6% higher than conventional CNN's in diagnosing NFU using the DFU dataset. Compared to the traditional CNN's, the proposed network requires lesser network parameters and is computationally efficient. The proposed network could benefit the clinicians for a second opinion about NFU diagnosis. The performance and robustness could be improved while testing the network with other open-source databases.