Coronary artery disease is one type of cardiovascular disease (CVD). According to the World Health Organisation (WHO), 31% of non-communicable disease (NCD) is from CVD. The build-up of lipids, plaques and calcification that embed in the inner wall of the artery that may result in narrowing the blood vessel. Therefore, the volume of blood flow is decreased. Moreover, the ruptured plaques and calcification may block the small arteries where the patient can get Abstract: Coronary artery disease (CAD) is part of the non-communicable disease (NCD) in cardiovascular disease (CVD). The blood vessel area became narrow when the calcification with the plaque embedded in the coronary artery inner wall. The radiologists and medical practitioners used visual inspection to detect calcification on IVUS image. The presence of calcification will not be able to do the measurement to calculate the maximum diameter and the maximum area for the patient coronary artery either before treatment or after treatment. More than 100 frames per patient is needed to analyse the location of the calcification. In this study, our aim is to detect the presence and the absence of the calcification in the coronary artery using intravascular ultrasound (IVUS) images with catheter frequency of 20MHz. The IVUS images used were the original Cartesian coordinate image and the polar reconstructed coordinate image. In this study, three types of convolutional neural network (CNN) using Directed Acyclic Graph networks, were used together with five types of classifiers. The dataset used to demonstrate our framework is Dataset B from MICCAI Challenge 2011 that consists of 2175 coronary artery disease IVUS image where 530 are IVUS images with calcification and 1645 are IVUS images without calcification. The cross validation for testing and training, the k-fold value used was 2, 3, 5 and 10. The performance measures for the ResNet-50, the ResNet-101 and the Inception-V3 model shows an excellent result using support vector machine classifier and discriminant analysis for both types of images. A better improvement using polar reconstructed coordinate image when using decision tree classifier and Naïve Bayes classifier whilst ResNet-101 architecture shows an excellent performance measure when applying images polar reconstructed images when using k-nearest neighbor classifier. However, Naïve Bayes classifier has an excellent result when using Inception-V3 architecture.
In this paper, we present our ongoing work on glaucoma classification using fundus images. The approach makes use of texture analysis based on Binary Robust Independent Elementary Features (BRIEF). This texture measurement is chosen because it can address the illumination issues of the retinal images and has a lower degree of computational complexity than most of the existing texture measurement methods currently used in the literature. Contrary to other approaches, the texture measures are extracted from the whole retina image without targeting any specific region. The method was tested on a set of 196 images composed of 110 healthy retina images and 86 glaucomatous images and achieved an area under curve (AUC) of 84%. A comparison performance with other texture measurements is also included, which shows our method to be superior.
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