Pneumonia Detection Proposing a Hybrid Deep Convolutional Neural Network Based on Two Parallel Visual Geometry Group Architectures and Machine Learning Classifiers

Abstract

Pneumonia is an acute respiratory infection that has led to significant deaths of people worldwide. This lung disease is more common in people older than 65 and children under five years old. Although the treatment of pneumonia can be challenging, it can be prevented by early diagnosis using Computer-Aided Diagnosis (CAD) systems. Chest X-Rays (CXRs) are currently the primary imaging tool for detection of pneumonia, which are widely used by radiologists. While the standard approach of detecting pneumonia is based on clinicians' decisions, various Deep Learning (DL) methods have been developed for detection of pneumonia considering CAD system. In this regard, a novel hybrid Convolutional Neural Network (CNN) model is proposed using three classification approaches. In the first classification approach, Fully-Connected (FC) layers are utilized for the classification of CXR images. This model is trained for several epochs and the weights that result in the highest classification accuracy are saved. In the second classification approach, the trained optimized weights are utilized to extract the most representative CXR image features and Machine Learning (ML) classifiers are employed to classify the images. In the third classification approach, an ensemble of the proposed classifiers is created to classify CXR images. The results suggest that the proposed ensemble classifier using Support Vector Machine (SVM) with Radial Basis Function (RBF) and Logistic Regression (LR) classifiers has the best performance with 98.55% accuracy. Ultimately, this model is deployed to create a web-based CAD system to assist radiologists in pneumonia detection with a significant accuracy.