Comparison of some CNN architectures for detecting cardiomegaly from chest X-ray images

In medical image analysis, deep learning and convolutional neural networks (CNN) are widely employed, particularly in tasks such as classification and segmentation. This study specifically addresses their application in healthcare for detecting cardiomegaly, a condition characterized by an enlarged heart, often related to factors such as hypertension or coronary artery diseases. The primary objective is to develop an algorithm to identify cardiomegaly in chest X-ray images, constituting a binary classification problem (whether the image exhibits cardiomegaly or not). Using the CXR8 dataset from the National Institute of Health Clinical Center, comprising 2\,776 cardiomegaly images and 60\,361 no finding images, the inputs are labeled images, and the outputs are the corresponding labels (Cardiomegaly or No Finding). Employing Keras and TensorFlow Python libraries, we aim to construct a CNN model that excels in binary classification, distinguishing between cardiomegaly and no finding in chest X-ray images.