COVID-19 and Pneumonia detection and web deployment from CT scan and X-ray images using deep learning

• Published in: PloS one Vol. 19; no. 7; p. e0302413
• Main Authors: Islam, Nahid, Mohsin, Abu S. M., Choudhury, Shadab Hafiz, Shaer, Tazwar Prodhan, Islam, Md. Adnan, Sadat, Omar, Taz, Nahid Hossain
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 08.07.2024; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Automation; Bacterial pneumonia; Betacoronavirus; Biology and Life Sciences; Computed tomography; Computer and Information Sciences; Coronavirus Infections - diagnosis; Coronavirus Infections - diagnostic imaging; COVID-19; COVID-19 - diagnostic imaging; CT imaging; Data mining; Datasets; Deep Learning; Diagnostic imaging; Disease transmission; Epidemics; Health aspects; Humans; Internet; Machine learning; Medical imaging; Medical imaging equipment; Medicine and Health Sciences; Neural networks; Pandemics; Patients; Pneumonia; Pneumonia - diagnosis; Pneumonia - diagnostic imaging; Pneumonia, Viral - diagnostic imaging; Radiation; Research and Analysis Methods; SARS-CoV-2 - isolation & purification; Severe acute respiratory syndrome coronavirus 2; Signs and symptoms; Taiwan; Tomography, X-Ray Computed - methods; Viral diseases; X-rays; Taiwan
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0302413

During the COVID-19 pandemic, pneumonia was the leading cause of respiratory failure and death. In addition to SARS-COV-2, it can be caused by several other bacterial and viral agents. Even today, variants of SARS-COV-2 are endemic and COVID-19 cases are common in many places. The symptoms of COVID-19 are highly diverse and robust, ranging from invisible to severe respiratory failure. Current detection methods for the disease are time-consuming and expensive with low accuracy and precision. To address such situations, we have designed a framework for COVID-19 and Pneumonia detection using multiple deep learning algorithms further accompanied by a deployment scheme. In this study, we have utilized four prominent deep learning models, which are VGG-19, ResNet-50, Inception V3 and Xception, on two separate datasets of CT scan and X-ray images (COVID/Non-COVID) to identify the best models for the detection of COVID-19. We achieved accuracies ranging from 86% to 99% depending on the model and dataset. To further validate our findings, we have applied the four distinct models on two more supplementary datasets of X-ray images of bacterial pneumonia and viral pneumonia. Additionally, we have implemented a flask app to visualize the outcome of our framework to show the identified COVID and Non-COVID images. The findings of this study will be helpful to develop an AI-driven automated tool for the cost effective and faster detection and better management of COVID-19 patients.