A novel solution of using deep learning for left ventricle detection: Enhanced feature extraction

Kiran Sharma, Abeer Alsadoon, P. W.C. Prasad, Thair Al-Dala'in, Tran Quoc Vinh Nguyen, Pham Duong Thu Hang

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


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.
Original languageEnglish
Article number105751
Pages (from-to)1-14
Number of pages14
JournalComputer Methods and Programs in Biomedicine
Early online date15 Sept 2020
Publication statusPublished - Dec 2020


Dive into the research topics of 'A novel solution of using deep learning for left ventricle detection: Enhanced feature extraction'. Together they form a unique fingerprint.

Cite this