Mubasshar, Abdullah  <br/><br/>
Cardio Vision / 
Abdullah Mubasshar, Umer Faisal, Muhammad Furqan, Muhammad Bin Asim. 
 - MCS, NUST   Rawalpindi   2024 
 - 149 p 
<br/><br/>Coronary artery disease (CAD) remains a leading cause of mortality worldwide, demanding<br/>efficient and accurate diagnostic tools. CardioVision aims to revolutionize CAD diagnosis through<br/>a DL-based web application that analyzes Coronary CT Angiography (CCTA) images. Leveraging<br/>a deep learning model, CardioVision employs advanced image enhancement techniques and<br/>convolutional neural networks (CNNs) to detect CAD. The model was trained using publicly<br/>available datasets, and further validated for high accuracy and reliability. The proposed CAD<br/>detection model aids radiologists and cardiologists in early identification of cardiac disease. Recent<br/>models for CAD detection require high computational resources and large image datasets. Thus,<br/>this study aims to develop a CNN-based CAD detection model. The Aquila optimization technique<br/>is utilized to optimize the hyperparameters of the UNet++ model for CAD prediction. This<br/>proposed method and hyperparameter tuning approach not only reduce computational costs but<br/>also enhance the performance of the UNet++ model. Our study findings conclude that the proposed<br/>model can be used to identify CAD with limited resources. 
<br/><br/><label>Subjects--Topical Terms: </label><br/>UG BESE
<br/><br/><label>Subjects--Geographic Terms: </label><br/>BESE-26 
<br/><br/><label>Dewey Class. No.: </label>005.1,MUB