An Efficient CNN Model for Automatic Diagnosis of Cardiomegaly from Chest Radiographic Images

Abstract

This work presents an algorithm for the automatic detection of cardiomegaly on CXR images. Cardiomegaly is a medical condition in which the heart becomes enlarged than the actual and the efficiency of the heart would decrease and sometimes congestive heart failure occurs. Although there could be numerous reasons, high blood pressure and coronary artery disease are the main causes of cardiomegaly. Hence, the main intention of this work is to develop a CNN based model to efficiently identify the presence of cardiomegaly abnormality. The learning phase of the model is achieved by using CXRs that are extracted from the publically available “chest x-ray14” medical dataset and to compute the proposed model performance, an experimental platform is designed and implemented in the MATLAB tool. We have trained the model with 100, 120, 150, and 200 epochs. But the trained model with 120 epochs shows a revolutionary outcome. The acquired accuracies of 100,120,150 and 200 epochs are 84.69%, 98.00%, 89.09% and 87.64% respectively. However, many approaches have been developed for cardiomegaly identification but the proposed model shows record performance.