Deep learning‐based high‐dimensional multiple regression estimator for chest x‐ray image classification in rapid cardiomegaly screening

Abstract

Chest X‐ray (CXR) examination is a common first‐line, non‐invasive, and rapid screening method in clinical examinations. The posteroanterior (PA) and anteroposterior (AP) view modes can both be used to detect related cardiopulmonary diseases, such as pneumonitis, tuberculosis, pulmonary fibrosis, lung tumors, and cardiomegaly. Compared with cardiac computed tomography and cardiac magnetic resonance imaging methods, CXR examination has a short scanning duration and costs less, and is suitable for routine and follow‐up health examinations. Cardiomegaly is an asymptomatic disease in the early stage and cannot be detected through electrocardiography measurements. Thus, early cardiomegaly classes detections, such as cardiac hypertrophy and ventricular dilatation, can help make decisions regarding drug treatments and surgeries. In addition, an automatic assistive tool is required to differentiate between normal individuals and those with cardiomegaly to address the problem of manual inspection and labor shortage. Hence, PA view‐based CXR classification is used to develop a deep learning (DL) ‐ based high‐dimensional multiple regression analysis (MRA) model for CXR image classification in rapid cardiomegaly screening. This multilayer network model uses a two‐channel three‐layer convolution– normalization–pooling process with two‐dimensional (2D) multi convolution operations to enhance images and to extract feature patterns; and then a one‐dimensional feature conversion is used to estimate the four coordinate points of the maximal horizontal cardiac diameter (MHCD) and maximal horizontal thoracic diameter (MHTD), which can be used to estimate cardiothoracic ratio and detect cardiomegaly. For experimental tests, the training and testing datasets are collected from the National Institutes of Health CXR Image Database (Clinical Center, USA), and 10‐fold cross‐validation was used for model evaluation in terms of precision (%), recall (%), accuracy (%), and F1 score. These indexes are used to evaluate the feasibility of the proposed MRA estimator. In addition, the performances of the proposed model are compared with those of conventional DL‐based multilayer classifiers.This article is protected by copyright. All rights reserved.