Cardiac magnetic resonance image diagnosis of hypertrophic obstructive cardiomyopathy based on a double-branch neural network. (March 2021)

• Record Type: Journal Article
• Authors: You, Yuanbing; Viktorovich, Lysenko Andrey; Qiu, Jiawei; Nikolaevich, Kosenkov Alexander; Vladimirovich, Belov Yuri
• Is Part Of: Computer methods and programs in biomedicine. Volume 200(2021)
• Publication Date: 2021-03
• Subjects: Cardiac magnetic resonance imaging -- Cardiomyopathy recognition -- Double-branch Network -- Hypertrophic obstructive cardiomyopathy -- Deep learning; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105889
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A double-branch neural network CMR-based hypertrophic obstructive cardiomyopathy recognition algorithm is implemented. CMR imaging automatic recognition algorithm is compared with traditional classification algorithms. The accuracy of our proposed method is approximately 10% higher than the traditional neural networks. Multi-task dual-branch cyclic neural network can capture static morphological and motion characteristics of heart. Abstract: Objective: Cardiac magnetic resonance (CMR) imaging is a well-established technique for diagnosis of hypertrophic obstructive cardiomyopathy (HOCM) and evaluation of cardiac function, but the process is complicated and time consuming. Therefore, this paper proposes a cardiomyopathy recognition algorithm using a multi-task learning mechanism and a double-branch deep learning neural network. Method: We implemented a double-branch neural network CMR-based HOCM recognition algorithm. Compared with the traditional classification algorithms such as the ResNet, DenseNet network, contrast the accuracy of network classification of cardiomyopathy is higher by 10.11%. Result: The loss curve of the algorithm basically converges in 100 rounds, and the convergence speed of the algorithm is twice that of the traditional algorithm. The accuracy of this algorithm to classify cardiomyopathy is 96.79%, and the sensitivity is 95.24%, which is 10.11% higher than the conventional algorithm. Conclusion: The CMR imaging automatic recognition algorithm for HOCM capture static morphological and motion characteristics of the heart, and comprehensively enhances recognition accuracy when the sample size is limited.