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This article is cited in 7 scientific papers (total in 7 papers)
Topical issues of biophotonics
Application of deep neural networks to improve diagnostic accuracy of rheumatoid arthritis using diffuse optical tomography
Y. Fengab, D. Lighterb, L. Zhanga, Y. Wanga, H. Dehghanib a College of Computer Science, Sichuan University, China
b Imaging Lab of the School of Computer Science, University of Birmingham, UK
Abstract:
A set of deep neural network models for rheumatoid arthritis (RA) classification using a highway network, a convolutional neural network and a residual network is proposed based on the data of diffuse optical tomography (DOT) utilising near-infrared light, which ensures early diagnosis of pathophysiological changes resulting from inflammation. A numerical model of the finger is used to generate images to overcome the inherent problem of insufficient clinical DOT images available. The proposed deep neural network models are applied to automatically classify simulated DOT images of inflamed and non-inflamed joints and transfer learning is also used to improve the performance of the classification. The results demonstrate that all three deep neural network methods improve the diagnostic accuracy as compared to the widely applied support vector machine (SVM), especially for high inter-subject variability databases. In cases of distinct modelled severity of disease, residual network achieved the highest accuracy (> 99%), and both of highway and convolutional neural networks reached 99%, respectively. However, as the severity of the modelled disease is reduced, this accuracy is reduced to 75.2% for residual networks. The results indicate that transfer learning can improve the performance of deep neural network methods on RA classification from DOT data and highlight their potential as a computer aided tool in DOT diagnostic systems.
Keywords:
rheumatoid arthritis diagnosis, diffuse optical tomography, finger joints, deep neural networks, medical image classification.
Received: 31.10.2019
Citation:
Y. Feng, D. Lighter, L. Zhang, Y. Wang, H. Dehghani, “Application of deep neural networks to improve diagnostic accuracy of rheumatoid arthritis using diffuse optical tomography”, Kvantovaya Elektronika, 50:1 (2020), 21–32 [Quantum Electron., 50:1 (2020), 21–32]
Linking options:
https://www.mathnet.ru/eng/qe17171 https://www.mathnet.ru/eng/qe/v50/i1/p21
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Abstract page: | 232 | Full-text PDF : | 115 | References: | 23 | First page: | 10 |
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