Detecting Cardiomegaly from CXR Images Using a 2D and 1D Convolutional Neural Network-Based Classifier

^{1 2 3 4} Department of Computer Science & Engineering, K G Reddy College of Engineering & Technology, Hyderabad, Telangana India.
⁵ Assistant Professor, Department of Computer Science and Engineering, GRIET, Bachupally, Hyderabad, Telangana.
⁶ Uttaranchal Instiiute of Technology, Uttaranchal University, Dehradun, 248007

^* Corresponding Author: lrgupta528@gmail.com

Abstract

A disorder called cardiomegaly has no symptoms. Heart hypertrophy and ventricular hypertrophy are two subtypes of the early symptoms of cardiac hypertrophy. Blowup, include pulsations, tightness in the chest, and shortness of breath. Their fundamental causes and therapeutic approaches differ. Making decisions on when to provide drugs and execute operations can be aided by the early detection of cardiomegaly. Along with the problems with home inspection. Similar to how time-consuming it is and how visitors and mortal interpretations are needed, a supporting tool is needed to automatically detect and distinguish between a normal heart and an enlarged heart.In medical procedures Based on examinations of chest X-rays (CXR) in anterior poster anterior view, this study suggests merging Convolution neural network, 2D and 1D grounded classifiers for quick cardiomegaly detection. The initial feature extraction and pattern recognition tests were performed using the original CXR image is enhanced and undesired noises are removed using the 2D and 1D convolution methods as well as a multilayer linked classification network. The classifier’s performance is validated using K-fold cross-validation after it has been trained using Using the testing dataset, the training dataset was analyzed. Recall, accuracy, precision, and F1 score of the rapid-fire cardiomegaly screening performance are demonstrated by experimental results.