Automated Methods for Detection and Classification Pneumonia based on X-Ray Images Using Deep Learning

TL;DR

This paper compares various deep learning architectures for automatic detection and classification of pneumonia from X-ray and CT images, demonstrating high accuracy with certain models like Resnet50, MobileNet_V2, and Inception_Resnet_V2.

Contribution

It provides a comparative analysis of multiple deep CNN architectures for pneumonia detection, highlighting the most effective models for medical image classification.

Findings

Resnet50, MobileNet_V2, and Inception_Resnet_V2 achieve over 96% accuracy.

Xception, VGG16, VGG19, Inception_V3, DenseNet201 perform below 84% accuracy.

Deep learning models can significantly aid rapid pneumonia diagnosis.

Abstract

Recently, researchers, specialists, and companies around the world are rolling out deep learning and image processing-based systems that can fastly process hundreds of X-Ray and computed tomography (CT) images to accelerate the diagnosis of pneumonia such as SARS, COVID-19, and aid in its containment. Medical images analysis is one of the most promising research areas, it provides facilities for diagnosis and making decisions of a number of diseases such as MERS, COVID-19. In this paper, we present a comparison of recent Deep Convolutional Neural Network (DCNN) architectures for automatic binary classification of pneumonia images based fined tuned versions of (VGG16, VGG19, DenseNet201, Inception_ResNet_V2, Inception_V3, Resnet50, MobileNet_V2 and Xception). The proposed work has been tested using chest X-Ray & CT dataset which contains 5856 images (4273 pneumonia and 1583 normal). As result we can conclude that fine-tuned version of Resnet50, MobileNet_V2 and Inception_Resnet_V2 show highly satisfactory performance with rate of increase in training and validation accuracy (more than 96% of accuracy). Unlike CNN, Xception, VGG16, VGG19, Inception_V3 and DenseNet201 display low performance (more than 84% accuracy).