A modified generative adversarial networks method for assisting the diagnosis of deep venous thrombosis complications in stroke patients - Ying Wang; Kaipeng Wang; Jinjun Hou; Ziqi Ye; Changsheng Lin; Xueping Li (Scientific Reports)
Abstract Discriminate deep vein thrombosis, one of the complications in early stroke patients, in order to assist in diagnosis. We have constructed a new method called ACWGAN by combining ACGAN and WGAN methods for data augmentation to to enhance the data of stroke early rehabilitation patients admitted to Nanjing First Hospital from 2017 to 2021, followed by analysis of complications, and compared it with 20 other commonly used data augmentation methods. A total of 7110 patients were included in the analysis of this study, the AUC value of the discriminative model ranges from 0.688 to 0.805 after data augmentation using traditional machine learning methods. When deep learning methods (GAN-based methods) are employed, the AUC value can surpass 0.866. Our proposed ACWGAN method maintains efficiency comparable to GAN, ACGAN, and WGAN, while achieving an even higher AUC value for the model trained on the augmented dataset, exceeding 0.938. The ACWGAN method effectively improves the diversity and accuracy of the model while ensuring the stability of the network, indicating that it can accurately assist in diagnosing the prevalence of DVT in patients.