000			01707nam a22001817a 4500
003			NUST
008			240920b ||||| |||| 00| 0 eng d
082			_a005.1,MUB
100			_aMubasshar, Abdullah _9125922
245			_aCardio Vision / _cAbdullah Mubasshar, Umer Faisal, Muhammad Furqan, Muhammad Bin Asim.
260			_aMCS, NUST _bRawalpindi _c2024
300			_a149 p
505			_aCoronary artery disease (CAD) remains a leading cause of mortality worldwide, demanding efficient and accurate diagnostic tools. CardioVision aims to revolutionize CAD diagnosis through a DL-based web application that analyzes Coronary CT Angiography (CCTA) images. Leveraging a deep learning model, CardioVision employs advanced image enhancement techniques and convolutional neural networks (CNNs) to detect CAD. The model was trained using publicly available datasets, and further validated for high accuracy and reliability. The proposed CAD detection model aids radiologists and cardiologists in early identification of cardiac disease. Recent models for CAD detection require high computational resources and large image datasets. Thus, this study aims to develop a CNN-based CAD detection model. The Aquila optimization technique is utilized to optimize the hyperparameters of the UNet++ model for CAD prediction. This proposed method and hyperparameter tuning approach not only reduce computational costs but also enhance the performance of the UNet++ model. Our study findings conclude that the proposed model can be used to identify CAD with limited resources.
650			_aUG BESE _9114271
651			_aBESE-26 _9125902
700			_aSupervisor Mobeena Shahzad _9114311
942			_2ddc _cPR
999			_c611586 _d611586