• Sharadhi B
• Sanjana Krishna
• Sejal Elizabeth

This project represents a significant leap forward in cardiovascular disease diagnosis and management by aiming to enhance the prediction of blockage detection in angiograms. At its core, the project seeks to leverage the power of deep learning (DL) techniques and Convolutional Neural Networks to substantially improve the accuracy of angiogram analysis, with a specific focus on identifying blockages. This ambitious endeavour involves training the algorithms on meticulously annotated angiogram datasets, enabling them to discern intricate patterns indicative of blockages. Furthermore, the project will employ cutting-edge techniques such as feature extraction and deep learning methodologies to further refine the blockage identification process. By enhancing the accuracy of blockage detection in angiograms, this project has the potential to revolutionize cardiovascular care, leading to more precise diagnoses and more effective treatment strategies, ultimately improving patient outcomes.

Deep Learning, Angiograms

"Design And Analysis Of CNN Based Heart Blockage Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.f751-f755, May-2024, Available :http://www.jetir.org/papers/JETIR2405584.pdf

"Design And Analysis Of CNN Based Heart Blockage Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppf751-f755, May-2024, Available at : http://www.jetir.org/papers/JETIR2405584.pdf