Dataset Information

Bi-Level Prediction Model for Screening COVID-19 Patients Using Chest X-Ray Images

ABSTRACT: The ongoing pandemic due to coronavirus disease, commonly abbreviated as COVID-19, has unleashed a major health crisis across the world. Although multiple vaccines have emerged, large scale vaccination have proven to be a major challenge, especially in developing nations. As a result, early detection still remains a crucial aspect of containing the spread of the virus. The popularly used test for COVID-19 is limited by the availability of test kits and is time-consuming. This has prompted researchers to use chest x-ray (CXR) and chest tomography (CT) scan images of subjects to predict COVID. Many COVID-19 patients also suffer from viral Pneumonia caused by SARS-CoV2 virus. Hence, distinguishing between bacterial and non-COVID Pneumonia is of paramount importance for proper diagnosis of the patients. To this end, in the present work, we have developed a bi-level prediction model of the subjects into normal, Pneumonia and COVID-19 patients by using a shallow learner based classifier on features extracted by VGG19 from the CXR images. The model is used on 3168 images distributed among normal, Pneumonia and COVID classes. We have created a dataset by collating CXR images from various sources like SIRM COVID-19 Database, Chest Imaging (Twitter), COVID-chestxray-dataset and Chest X-Ray Images. The experimental results confirm the superiority of the proposed model (99.26% accuracy) over the best performing single-level classification method (96.74% accuracy). This result is also at par with the many state-of-the-art methods mentioned in literature. The source code is available in the link https://github.com/sdrxc/Bi-level-Prediction-Model-for-Screening-COVID-19-from-Chest-X-ray-Images.

SUBMITTER: Das S

PROVIDER: S-EPMC8084620 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:The COVID-19 pandemic is causing a major outbreak in more than 150 countries around the world, having a severe impact on the health and life of many people globally. One of the crucial step in fighting COVID-19 is the ability to detect the infected patients early enough, and put them under special care. Detecting this disease from radiography and radiology images is perhaps one of the fastest ways to diagnose the patients. Some of the early studies showed specific abnormalities in the chest radiograms of patients infected with COVID-19. Inspired by earlier works, we study the application of deep learning models to detect COVID-19 patients from their chest radiography images. We first prepare a dataset of 5000 Chest X-rays from the publicly available datasets. Images exhibiting COVID-19 disease presence were identified by board-certified radiologist. Transfer learning on a subset of 2000 radiograms was used to train four popular convolutional neural networks, including ResNet18, ResNet50, SqueezeNet, and DenseNet-121, to identify COVID-19 disease in the analyzed chest X-ray images. We evaluated these models on the remaining 3000 images, and most of these networks achieved a sensitivity rate of 98% ( ± 3%), while having a specificity rate of around 90%. Besides sensitivity and specificity rates, we also present the receiver operating characteristic (ROC) curve, precision-recall curve, average prediction, and confusion matrix of each model. We also used a technique to generate heatmaps of lung regions potentially infected by COVID-19 and show that the generated heatmaps contain most of the infected areas annotated by our board certified radiologist. While the achieved performance is very encouraging, further analysis is required on a larger set of COVID-19 images, to have a more reliable estimation of accuracy rates. The dataset, model implementations (in PyTorch), and evaluations, are all made publicly available for research community at https://github.com/shervinmin/DeepCovid.git.

Dataset Information

Bi-Level Prediction Model for Screening COVID-19 Patients Using Chest X-Ray Images

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets