Survey On Techniques For Cardiomegaly Prediction By Chest X-ray Images

Abstract

Cardiomegaly is a condition characterized by an enlarged heart, which can be indicative of various underlying health issues. Early diagnosis of this condition lessens the patient's repercussions. The paper gives a thorough overview of the disease, its classifications, algorithms, and methods employed emphasizing the challenges encountered in this field. Traditional methods of diagnosing cardiomegaly rely on medical imaging techniques such as echocardiography or chest X-rays, which can be time-consuming and require specialized expertise to interpret. However, recent advances in deep learning algorithms have shown promise in accurately identifying cardiomegaly and its underlying causes from medical images. The use of deep learning algorithms in the diagnosis of cardiomegaly has the potential to improve both the speed and accuracy of diagnosis, leading to better patient outcomes and more efficient use of healthcare resources. Moreover, deep learning algorithms can potentially identify subtle changes in heart size over time, allowing for earlier detection and treatment of cardiomegaly.