Convolutional Neural Network Based Classification of Patients with Pneumonia using X-ray Lung Images
Hicham Moujahid, Bouchaib Cherradi, Oussama El Gannour, Lhoussain Bahatti, Oumaima Terrada, Soufiane Hamida
Abstract
Analysis and classification of lung diseases using X-ray images is a primary step in the procedure of pneumonia diagnosis, especially in a critical period as pandemic of COVID-19 that is type of pneumonia Therefore, an automatic method with high accuracy of classification is needed to perform classification of lung diseases due to the increasing number of cases Convolutional Neural Networks (CNN) based classification has gained a big popularity over the last few years because of its speed and level of accuracy on the image’s classification tasks Through this article, we propose an implementation a CNN-based classification models using transfer learning technique to perform pneumonia detection and compare the results in order to detect the best model for the task according to certain parameters As this has become a fast expanding field, there are several models but we will focus on the best outperforming algorithms according to their architecture, length and type of layers and evaluation parameters for the classification tasks Firstly, we review the existing conventional methods and deep learning architectures used for segmentation in general Next, we perform a deep performance and analysis based on accuracy and loss function of implemented models A critical analysis of the results is made to highlight all important issues to improve © 2020 ASTES Publishers All rights reserved