A novel solution of using deep learning for left ventricle detection: Enhanced feature extraction. (December 2020)

• Record Type: Journal Article
• Authors: Sharma, Kiran; Alsadoon, Abeer; Prasad, P.W.C.; Al-Dala'in, Thair; Nguyen, Tran Quoc Vinh; Pham, Duong Thu Hang
• Is Part Of: Computer methods and programs in biomedicine. Volume 197(2020)
• Publication Date: 2020-12
• Subjects: Deep learning -- Convolutional neural network -- Left ventricle -- Myocardium -- Overfitting -- Normalization Feature extraction; 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.2020.105751
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Data augmentation performed to create a new sample image by performing transformation like flipping, rotation etc., to solve the problem of overfitting created due to the model complexity of a larger number of parameters. Extra convolutional layer added to extract more important features and increase the overall accuracy in the detection of the left ventricle. Drop out layer introduced during the feature extraction process to enable the model to learn only the important features and drop the irrelevant features to fix the problem overfitting and increases generalizability. Abstract: Background and aim: deep learning algorithms have not been successfully used for the left ventricle (LV) detection in echocardiographic images due to overfitting and vanishing gradient descent problem. This research aims to increase accuracy and improves the processing time of the left ventricle detection process by reducing the overfitting and vanishing gradient problem. Methodology: the proposed system consists of an enhanced deep convolutional neural network with an extra convolutional layer, and dropout layer to solve the problem of overfitting and vanishing gradient. Data augmentation was used for increasing the accuracy of feature extraction for left ventricle detection. Results: four pathological groups of datasets were used for training and evaluation of the model: heart failure without infarction, heart failure with infarction, and hypertrophy, and healthy. The proposed model provided an accuracy of 94% in left ventricle detection for all the groups compared to the other current systems. The results showed that the processing time was reduced from 0.45 s to 0.34 s in an average. Conclusion: the proposed system enhances accuracy and decreases processing time in the left ventricle detection. This paper solves the issues of overfitting of the data.