Coronary artery disease classification using ConvMixer based classifier from CT angiography images
C. Rajeev, Karthika Natarajan
TL;DR
This paper introduces a deep learning model using ConvMixer to classify coronary artery disease from CT angiography images, aiming to reduce the need for invasive procedures.
Contribution
The novel contribution is the integration of ConvMixer with median filter and morphological operations for CAD classification from CT images.
Findings
The model achieved 96.30% accuracy, 94.39% sensitivity, and 99.16% specificity using morphological operations and ConvMixer.
DL heat maps were used to explain the model's decisions, enhancing diagnostic transparency.
The system reduces manual involvement and supports medical professionals in CAD diagnosis.
Abstract
Coronary artery disease (CAD) has recently emerged as a predominant source of morbidity and death worldwide. Assessing the existence and severity of CAD in people is crucial for determining the optimal treatment strategy. Currently, computed tomography (CT) delivers excellent spatial resolution pictures of the heart and coronary arteries at a rapid pace. Conversely, several problems exist in the analysis of cardiac CT images for indications of CAD. Research investigations employ machine learning (ML) and deep learning (DL) techniques to achieve high accuracy and consistent performance, hence addressing existing restrictions. This research proposes convMixer with median filter and morphological operations for the classification of the coronary artery disease from computed tomography angiography images. A total of 5,959 CT angiography images were used for classification. The model…
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Taxonomy
TopicsAdvanced X-ray and CT Imaging · Cardiac Imaging and Diagnostics · Radiomics and Machine Learning in Medical Imaging