Title:Detection of Abnormality in Coronary Artery Magnetic Resonance Imaging
using Bit Plane Slicing and Deep Learning
Volume: 20
Author(s): Le Nhi Lam Thuy, Vo Hoang Trong, Huynh Trung Hieu and Pham The Bao*
Affiliation:
- Information Science Faculty, Sai Gon University, HCM City, Vietnam
Keywords:
Detecting abnormality, Plane information, CNN, Voting, Coronary artery, MRI.
Abstract:
Introduction:
This paper presents a novel approach for detecting abnormality in coronary arteries using MRI data in RGB images. The study evaluates the test
accuracy of the weak classifiers and the test accuracy and F1 score of the strong classifier.
Methods:
The method involves separating the image into information planes, including R, G, and B color space, or bit-planes, and training a VGG-like
convolutional neural network model on each plane separately, referred to as a “weak classifier.” The classification results of these planes are
aggregated using a proposed soft voting method, forming a “strong classifier,” with the weights for the aggregation determined by the model's
performance on the training set.
Results:
The results indicate that the strong classifier achieves a test accuracy and F1 score of around 68% to 74% on our private coronary artery dataset.
Moreover, by aggregating the top three highest bit-plane levels in a grayscale image, the accuracy is slightly lower than that of the three color
spaces but requires a significantly smaller CNN model of nearly 4M parameters.
Conclusion:
The potential of bit-planes in reducing model storage costs is suggested. This approach holds promise for improving the detection of abnormalities
in coronary arteries using MRI data.
