Journal
MATHEMATICAL BIOSCIENCES AND ENGINEERING
Volume 19, Issue 10, Pages 10037-10059Publisher
AMER INST MATHEMATICAL SCIENCES-AIMS
DOI: 10.3934/mbe.2022469
Keywords
ankle instability; computer-assisted detection; gait features; Dual Generative Adversarial Networks (Dual-GAN); Long Short-Term Memory (LSTM)
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Funding
- Peking University Third Hospital-Haidian Innovation and Transformation Project [Y74482-09]
- National Natural Science Foundation of China [61801019]
- China Scholarship Council [201906465021]
- Fundamental Research Funds for the University of Science and Technology Beijing [FRF-BD-19-012A]
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This paper introduces a new approach to extract significant features from a small dataset in order to improve computer-assisted diagnosis using deep learning. By utilizing Dual Generative Adversarial Networks and modified Long Short-Term Memory algorithm, the authors were able to augment the training data and enhance the diagnosis of Chronic Ankle Instability patients.
Obtaining massive amounts of training data is often crucial for computer-assisted diagnosis using deep learning. Unfortunately, patient data is often small due to varied constraints. We develop a new approach to extract significant features from a small clinical gait analysis dataset to improve computer-assisted diagnosis of Chronic Ankle Instability (CAI) patients. In this paper, we present an approach for augmenting spatiotemporal and kinematic characteristics using the Dual Generative Adversarial Networks (Dual-GAN) to train a series of modified Long Short-Term Memory (LSTM) detection models making the training process more data-efficient. Namely, we use LSTM-, LSTM-Fully Convolutional Networks (FCN)-, and Convolutional LSTM-based detection models to identify the patients with CAI. The Dual-GAN enables the synthesized data to approximate the real data distribution visualized by the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm. Then we trained the proposed detection models using real data collected from a controlled laboratory study and mixed data from real and synthesized gait features. The detection models were tested in real data to validate the positive role in data augmentation as well as to demonstrate the capability and effectiveness of the modified LSTM algorithm for CAI detection using spatiotemporal and kinematic characteristics in walking. Dual-GAN generated efficient spatiotemporal and kinematic characteristics to augment the training set promoting the performance of CAI detection and the modified LSTM algorithm yielded an enhanced classification outcome to identify those CAI patients from a group of control subjects based on gait analysis data than any previous reports.
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