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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

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Project description:Recently, the infectious disease COVID-19 remains to have a catastrophic effect on the lives of human beings all over the world. To combat this deadliest disease, it is essential to screen the affected people quickly and least inexpensively. Radiological examination is considered the most feasible step toward attaining this objective; however, chest X-ray (CXR) and computed tomography (CT) are the most easily accessible and inexpensive options. This paper proposes a novel ensemble deep learning-based solution to predict the COVID-19-positive patients using CXR and CT images. The main aim of the proposed model is to provide an effective COVID-19 prediction model with a robust diagnosis and increase the prediction performance. Initially, pre-processing, like image resizing and noise removal, is employed using image scaling and median filtering techniques to enhance the input data for further processing. Various data augmentation styles, such as flipping and rotation, are applied to capable the model to learn the variations during training and attain better results on a small dataset. Finally, a new ensemble deep honey architecture (EDHA) model is introduced to effectively classify the COVID-19-positive and -negative cases. EDHA combines three pre-trained architectures like ShuffleNet, SqueezeNet, and DenseNet-201, to detect the class value. Moreover, a new optimization algorithm, the honey badger algorithm (HBA), is adapted in EDHA to determine the best values for the hyper-parameters of the proposed model. The proposed EDHA is implemented in the Python platform and evaluates the performance in terms of accuracy, sensitivity, specificity, precision, f1-score, AUC, and MCC. The proposed model has utilized the publicly available CXR and CT datasets to test the solution's efficiency. As a result, the simulated outcomes showed that the proposed EDHA had achieved better performance than the existing techniques in terms of Accuracy, Sensitivity, Specificity, Precision, F1-Score, MCC, AUC, and Computation time are 99.1%, 99%, 98.6%, 99.6%, 98.9%, 99.2%, 0.98, and 820 s using the CXR dataset.

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.

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

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