SOSPCNN: Structurally Optimized Stochastic Pooling Convolutional Neural Network for Tetralogy of Fallot Recognition. (2nd July 2021)

• Record Type: Journal Article
• Authors: Wang, Shui-Hua; Wu, Kaihong; Chu, Tianshu; Fernandes, Steven L.; Zhou, Qinghua; Zhang, Yu-Dong; Sun, Jian
• Other Names: Zhong Shan Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2021(2021)
• Publication Date: 2021-07-02
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2021/5792975
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : Aim . This study proposes a new artificial intelligence model based on cardiovascular computed tomography for more efficient and precise recognition of Tetralogy of Fallot (TOF). Methods . Our model is a structurally optimized stochastic pooling convolutional neural network (SOSPCNN), which combines stochastic pooling, structural optimization, and convolutional neural network. In addition, multiple-way data augmentation is used to overcome overfitting. Grad-CAM is employed to provide explainability to the proposed SOSPCNN model. Meanwhile, both desktop and web apps are developed based on this SOSPCNN model. Results . The results on ten runs of 10-fold crossvalidation show that our SOSPCNN model yields a sensitivity of 92.25 ± 2.19, a specificity of 92.75 ± 2.49, a precision of 92.79 ± 2.29, an accuracy of 92.50 ± 1.18, an F1 score of 92.48 ± 1.17, an MCC of 85.06 ± 2.38, an FMI of 92.50 ± 1.17, and an AUC of 0.9587. Conclusion . The SOSPCNN method performed better than three state-of-the-art TOF recognition approaches.