COVID-19 and Pneumonia Detection and Web Deployment from CT Scan and X-ray Images Using Deep Learning

Posted on:May 6, 2023 at 12:00 AM

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Full Paper  (Open-Access)

Abstract

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 cost-effective and faster detection and better management of COVID-19 patients.

Credits

Nahid Islam 1, Dr. Abu S. M. Mohsin 1 2, Shadab Hafiz Choudhury 1, Tazwar Prodhan Shaer 1, Adnan Islam 1, Omar Sadat 1, Nahid Hossain Taz 1

1: Department of Electrical and Electronic Engineering, Brac University
2: Corresponding Author

Citation

@article{islam_covid-19_2024,
    title = {{COVID}-19 and {Pneumonia} detection and web deployment from {CT} scan and {X}-ray images using deep learning},
    volume = {19},
    issn = {1932-6203},
    url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0302413},
    doi = {10.1371/journal.pone.0302413},
    language = {en},
    number = {7},
    urldate = {2024-12-16},
    journal = {PLOS ONE},
    author = {Islam, Nahid and Mohsin, Abu S. M. and Choudhury, Shadab Hafiz and Shaer, Tazwar Prodhan and Islam, Md Adnan and Sadat, Omar and Taz, Nahid Hossain},
    month = jul,
    year = {2024},
    note = {Publisher: Public Library of Science},
    keywords = {Bacterial pneumonia, Computed axial tomography, COVID 19, Deep learning, Pneumonia, Viral pneumonia, Virus testing, X-ray radiography},
    pages = {e0302413},
}