Detection of Abnormality in Coronary Artery Magnetic Resonance Imaging using Bit Plane Slicing and Deep Learning

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.

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.

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.

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.