DFU_QUTNet: diabetic foot ulcer classification using novel deep convolutional neural network

• Published in: Multimedia tools and applications Vol. 79; no. 21-22; pp. 15655 - 15677
• Main Authors: Alzubaidi, Laith, Fadhel, Mohammed A., Oleiwi, Sameer R., Al-Shamma, Omran, Zhang, Jinglan
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.06.2020; Springer Nature B.V
• Subjects: Artificial neural networks; Back propagation; Classification; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Diabetes; Diagnosis; Feature extraction; Foot diseases; Leg ulcers; Machine learning; Multimedia Information Systems; Neural networks; Special Purpose and Application-Based Systems; Support vector machines; Deep learning; Diabetic foot ulcers; Convolutional neural networks; Transfer learning; Classification
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-019-07820-w

Diabetic Foot Ulcer (DFU) is the main complication of Diabetes, which, if not properly treated, may lead to amputation. One of the approaches of DFU treatment depends on the attentiveness of clinicians and patients. This treatment approach has drawbacks such as the high cost of the diagnosis as well as the length of treatment. Although this approach gives powerful results, the need for a remote, cost-effective, and convenient DFU diagnosis approach is urgent. In this paper, we introduce a new dataset of 754-ft images which contain healthy skin and skin with a diabetic ulcer from different patients. A novel Deep Convolutional Neural Network, DFU_QUTNet, is also proposed for the automatic classification of normal skin (healthy skin) class versus abnormal skin (DFU) class. Stacking more layers to a traditional Convolutional Neural Network to make it very deep does not lead to better performance, instead leading to worse performance due to the gradient. Therefore, our proposed DFU_QUTNet network is designed based on the idea of increasing the width of the network while keeping the depth compared to the state-of-the-art networks. Our network has been proven to be very beneficial for gradient propagation, as the error can be back-propagated through multiple paths. It also helps to combine different levels of features at each step of the network. Features extracted by DFU_QUTNet network are used to train Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers. For the sake of comparison, we have fine-tuned then re-trained and tested three pre-trained deep learning networks (GoogleNet, VGG16, and AlexNet) for the same task. The proposed DFU_QUTNet network outperformed the state-of-the-art CNN networks by achieving the F1-score of 94.5%.