tetano
Editor, Senior Moderator
PLoS One
. 2024 Jul 8;19(7):e0302413.
doi: 10.1371/journal.pone.0302413. eCollection 2024. COVID-19 and Pneumonia detection and web deployment from CT scan and X-ray images using deep learning
Nahid Islam[SUP] 1 [/SUP], Abu S M Mohsin[SUP] 1 [/SUP], Shadab Hafiz Choudhury[SUP] 1 [/SUP], Tazwar Prodhan Shaer[SUP] 1 [/SUP], Md Adnan Islam[SUP] 1 [/SUP], Omar Sadat[SUP] 1 [/SUP], Nahid Hossain Taz[SUP] 1 [/SUP]
Affiliations
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.
. 2024 Jul 8;19(7):e0302413.
doi: 10.1371/journal.pone.0302413. eCollection 2024. COVID-19 and Pneumonia detection and web deployment from CT scan and X-ray images using deep learning
Nahid Islam[SUP] 1 [/SUP], Abu S M Mohsin[SUP] 1 [/SUP], Shadab Hafiz Choudhury[SUP] 1 [/SUP], Tazwar Prodhan Shaer[SUP] 1 [/SUP], Md Adnan Islam[SUP] 1 [/SUP], Omar Sadat[SUP] 1 [/SUP], Nahid Hossain Taz[SUP] 1 [/SUP]
Affiliations
- PMID: 38976703
- 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.