Key result
Deep learning models enable accurate automated detection of cardiomegaly on chest X-rays.
Why the study?
Early detection of cardiomegaly is important to improve treatment and reduce complications, but automated detection methods using deep learning need evaluation.
May aid automated cardiomegaly detection on CXR; leaves open prospective validation before clinical adoption.
Cardiomegaly is an augmented (enlarged) heart. It is not a disease merely a sign of another condition. Cardiomegaly in the early stages, which is less severe, is called mild Cardiomegaly. These complications may give rise to conditions like Blood clots, Cardiac Arrest and sudden death, Heart failure, Heart murmur. Hence, detecting these kinds of states in the early stages helps in improving medications and reduce complications. In this paper, we will present various approaches available to detect this development using Deep learning and offer an automated system to detect the presence of Cardiomegaly in a patient. For the computerized system, we are using deep learning concepts such as U-Net and VGG16 and image enhancement (image prepossessing is required) methods like Unsharp Masking, Contrast Limited Adaptive Histogram, High-Frequency Emphasis Filtering. Accurate measure of CTR (cardiothoracic ratio) calculations can effectively diagnose the presence of Cardiomegaly in a patient.
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Prof. Vidyashree K P (2021) conducted a review in Cardiomegaly. Deep learning and image enhancement techniques was evaluated. Deep learning models such as U-Net and VGG16, along with image enhancement techniques, enable automated and accurate detection of cardiomegaly from chest X-rays.
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