Open Access

ARTICLE

Detection of Left Ventricular Cavity from Cardiac MRI Images Using Faster R-CNN

Zakarya Farea Shaaf^1,*, Muhammad Mahadi Abdul Jamil¹, Radzi Ambar¹, Ahmed Abdu Alattab^2,3, Anwar Ali Yahya^3,4, Yousef Asiri⁴
1 Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Parit Raja, Batu Pahat, 86400, Johor, Malaysia
2 Department of Computer Science, College of Science and Arts, Sharurah, Najran University, Najran, 61441, Saudi Arabia
3 Department of Computer Science, Faculty of Computer Science and Info. Systems, Thamar University, Dhamar, 87246, Yemen
4 Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia
* Corresponding Author: Zakarya Farea Shaaf. Email:

Computers, Materials & Continua 2023, 74(1), 1819-1835. https://doi.org/10.32604/cmc.2023.031900

Received 29 April 2022; Accepted 12 June 2022; Issue published 22 September 2022

Abstract

The automatic localization of the left ventricle (LV) in short-axis magnetic resonance (MR) images is a required step to process cardiac images using convolutional neural networks for the extraction of a region of interest (ROI). The precise extraction of the LV’s ROI from cardiac MRI images is crucial for detecting heart disorders via cardiac segmentation or registration. Nevertheless, this task appears to be intricate due to the diversities in the size and shape of the LV and the scattering of surrounding tissues across different slices. Thus, this study proposed a region-based convolutional network (Faster R-CNN) for the LV localization from short-axis cardiac MRI images using a region proposal network (RPN) integrated with deep feature classification and regression. The model was trained using images with corresponding bounding boxes (labels) around the LV, and various experiments were applied to select the appropriate layers and set the suitable hyper-parameters. The experimental findings show that the proposed model was adequate, with accuracy, precision, recall, and F1 score values of 0.91, 0.94, 0.95, and 0.95, respectively. This model also allows the cropping of the detected area of LV, which is vital in reducing the computational cost and time during segmentation and classification procedures. Therefore, it would be an ideal model and clinically applicable for diagnosing cardiac diseases.

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Keywords

Cardiac short-axis MRI images; automatic left ventricle localization; deep learning models; faster R-CNN

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