Project Abstract

This research addresses refining the segmentation of cardiac muscle cells, aiming with the goal of understanding progression of heart disease and improving the diagnosis, and treatment strategies in the medical field. The project?��s main goal is to develop new methodology to understand cell communication within the cardiac network. It aims to improve cell segmentation algorithms for identification of cardiomyocytes accurately using machine learning (ML)/deep learning (DL) approaches, namely convolutional neural networks (CNNs) and the U-Net architecture are widely used one. The research is focused on integrating analysis of data and image processing methods to improve the cell segmentation accuracy and their respective performance. The literature survey reviews current developments in cell segmentation approaches in a range of medical fields, highlighting the importance of AI-driven methods in enhancing accuracy and helping in patient outcomes. By combining theoretical understanding with useful techniques, this study adds to the ongoing research in biomedical by opening the way for more effective methods for the diagnosis and treatment of cardiac disease.

Keywords: Machine Learning in cardiology, AI in Medical Diagnosis, Biomedical Image Processing

 Conference Details

Session: Presentation Stream 1 at Presentation Slot 10

Location: GH049 at Tuesday 7th 13:30 – 17:00

Markers: Chen Hu (GTA), Simon Robinson

Course: MSc Data Science, Masters PG

Future Plans: I’m looking for work