Classification of Normal and Cardiomegaly Conditions in Chest Radiographs Using Cardio-Mediastinal Features.

In this study, an attempt has been made to differentiate normal and cardiomegaly using cardio-mediastinal ratiometric features and machine learning approaches. A total of 60 chest radiographs including normal and cardiomegaly subjects are considered from a public dataset. The images are preprocessed using edge aware contrast enhancement technique to improve the edge contrast of lung boundaries. The mediastinal, cardiac and thoracic widths and their ratiometric indices are computed to characterize the morphological variations. The features are fed to three different classifiers for the differentiation of normal and cardiomegaly. Results show that the Linear discriminant analysis classifier is found to perform better with average values of recall 88.7%, precision 88.8%, and area under the curve 91.9%. Hence, the proposed computer aided diagnostic approach appears to be clinically significant to distinguish normal and cardiomegaly especially in remote and resource - poor settings.