Cardio Vision / (Record no. 611585)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 01707nam a22001817a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | NUST |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240920b ||||| |||| 00| 0 eng d |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 005.1,MUB |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Mubasshar, Abdullah |
| 9 (RLIN) | 125922 |
| 245 ## - TITLE STATEMENT | |
| Title | Cardio Vision / |
| Statement of responsibility, etc. | Abdullah Mubasshar, Umer Faisal, Muhammad Furqan, Muhammad Bin Asim. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | MCS, NUST |
| Name of publisher, distributor, etc. | Rawalpindi |
| Date of publication, distribution, etc. | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 149 p |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 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. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | UG BESE |
| 9 (RLIN) | 114271 |
| 651 ## - SUBJECT ADDED ENTRY--GEOGRAPHIC NAME | |
| Geographic name | BESE-26 |
| 9 (RLIN) | 125902 |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor Mobeena Shahzad |
| 9 (RLIN) | 114311 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Project Report |
No items available.