Classification of Diabetic Foot Ulcers Using Class Knowledge Banks

Xu, Yi, Han, Kang, Zhou, Yongming, Wu, Jian, Xie, Xin, and Xiang, Wei (2022) Classification of Diabetic Foot Ulcers Using Class Knowledge Banks. Frontiers in Bioengineering and Biotechnology, 9. 811028.

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Diabetic foot ulcers (DFUs) are one of the most common complications of diabetes. Identifying the presence of infection and ischemia in DFU is important for ulcer examination and treatment planning. Recently, the computerized classification of infection and ischaemia of DFU based on deep learning methods has shown promising performance. Most state-of-the-art DFU image classification methods employ deep neural networks, especially convolutional neural networks, to extract discriminative features, and predict class probabilities from the extracted features by fully connected neural networks. In the testing, the prediction depends on an individual input image and trained parameters, where knowledge in the training data is not explicitly utilized. To better utilize the knowledge in the training data, we propose class knowledge banks (CKBs) consisting of trainable units that can effectively extract and represent class knowledge. Each unit in a CKB is used to compute similarity with a representation extracted from an input image. The averaged similarity between units in the CKB and the representation can be regarded as the logit of the considered input. In this way, the prediction depends not only on input images and trained parameters in networks but the class knowledge extracted from the training data and stored in the CKBs. Experimental results show that the proposed method can effectively improve the performance of DFU infection and ischaemia classifications.

Item ID: 73414
Item Type: Article (Research - C1)
ISSN: 2296-4185
Keywords: diabetic foot ulcer, image recongnition system, deep learning, infection and ischemia classification, knowledge learning
Date Deposited: 06 Apr 2022 08:21
FoR Codes: 32 BIOMEDICAL AND CLINICAL SCIENCES > 3206 Medical biotechnology > 320602 Medical biotechnology diagnostics (incl. biosensors) @ 50%
32 BIOMEDICAL AND CLINICAL SCIENCES > 3202 Clinical sciences > 320216 Orthopaedics @ 50%
SEO Codes: 20 HEALTH > 2001 Clinical health > 200101 Diagnosis of human diseases and conditions @ 100%
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