In this study, we investigate the detection of cardiomegaly on frontal chest radiographs
through two alternative deep-learning approaches-via anatomical segmentation and via …

We examined the feasibility of explainable computer-aided detection of cardiomegaly in
routine clinical practice using segmentation-based methods. Overall, 793 retrospectively …

Building a reliable and precise model for disease classification and identifying abnormal
sites can provide physicians assistance in their decision-making process. Deep learning …

Background Cardiomegaly is a relatively common incidental finding on chest X-rays; if left
untreated, it can result in significant complications. Using Artificial Intelligence for diagnosing …

The successful integration of neural networks in a clinical setting is still uncommon despite
major successes achieved by artificial intelligence in other domains. This is mainly due to …

This study investigates using deep convolutional neural networks (CNN) for automatic
detection of cardiomegaly in digital chest X-rays (CXRs). First, we employ and fine-tune …

Background Cardiothoracic ratio (CTR) is the ratio of the diameter of the heart to the
diameter of the thorax. An abnormal CTR (> 0.55) is often an indicator of an underlying …

We propose an automated method based on deep learning to compute the cardiothoracic
ratio and detect the presence of cardiomegaly from chest radiographs. We develop two …

Accurate identification and localization of multiple abnormalities are crucial steps in the
interpretation of chest X-rays (CXRs); however, the lack of a large CXR dataset with …

In this paper, we present an automated procedure to determine the presence of
cardiomegaly on chest X-ray image based on deep learning. The proposed algorithm …