Implementing Convolutional Neural Networks for Enhanced Detection of Cardiomegaly in Clinical Chest X-Ray Images

One important indicator of several underlying cardiovascular disorders is cardiomegaly, a disorder marked by heart enlargement. Effective management and therapy depend on early, precise identification. The application of Convolutional Neural Networks (CNNs) for cardiomegaly disease prediction using medical imaging data is investigated in this paper. Commonly used in the diagnosis of cardiomegaly, we deployed a CNN architecture specifically designed to process and evaluate chest X-ray images. Trained on a large dataset, the suggested model attained an accuracy of 80% in determining if cardiomegaly exists or absent. This performance shows how well CNN can support diagnostic procedures, therefore maybe increasing clinical results by early identification. Our results imply that, in the field of medical imaging for cardiovascular illnesses, CNNs can be a useful tool; still, more research and validations are required to increase diagnosis precision.