Dataset Information

A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images.

ABSTRACT: Corona virus disease (COVID-19) acknowledged as a pandemic by the WHO and mankind all over the world is vulnerable to this virus. Alternative tools are needed that can help in diagnosis of the coronavirus. Researchers of this article investigated the potential of machine learning methods for automatic diagnosis of corona virus with high accuracy from X-ray images. Two most commonly used classifiers were selected: logistic regression (LR) and convolutional neural networks (CNN). The main reason was to make the system fast and efficient. Moreover, a dimensionality reduction approach was also investigated based on principal component analysis (PCA) to further speed up the learning process and improve the classification accuracy by selecting the highly discriminate features. The deep learning-based methods demand large amount of training samples compared to conventional approaches, yet adequate amount of labelled training samples was not available for COVID-19 X-ray images. Therefore, data augmentation technique using generative adversarial network (GAN) was employed to further increase the training samples and reduce the overfitting problem. We used the online available dataset and incorporated GAN to have 500 X-ray images in total for this study. Both CNN and LR showed encouraging results for COVID-19 patient identification. The LR and CNN models showed 95.2-97.6% overall accuracy without PCA and 97.6-100% with PCA for positive cases identification, respectively.

SUBMITTER: Rasheed J

PROVIDER: S-EPMC7776293 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images.

Rasheed Jawad J Hameed Alaa Ali AA Djeddi Chawki C Jamil Akhtar A Al-Turjman Fadi F

Interdisciplinary sciences, computational life sciences 20210102 1

Corona virus disease (COVID-19) acknowledged as a pandemic by the WHO and mankind all over the world is vulnerable to this virus. Alternative tools are needed that can help in diagnosis of the coronavirus. Researchers of this article investigated the potential of machine learning methods for automatic diagnosis of corona virus with high accuracy from X-ray images. Two most commonly used classifiers were selected: logistic regression (LR) and convolutional neural networks (CNN). The main reason w ...[more]

PMID: 33387306

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Project description:The objective of the research article is to propose and validate a combination of machine learning and radiomics features to detect COVID-19 early and rapidly from chest X-ray (CXR) in presence of other viral/bacterial pneumonia and at different severity levels of diseases. It is vital to assess the performance of any diagnosis method on an independent data set and at very early stage of the disease when the disease severity of is very low. In such cases, most of the diagnosis methods fail. A total of 378 CXR images containing both normal lung and pneumonia (both COVID-19 and others lung conditions) were collected from publically available data set. 71 radiomics features for each lung segment were chosen from 100 extracted features based on Z-score heatmap and one way ANOVA test that can detect COVID-19. Three best performing classical machine learning algorithms during the training phase - 1) fine Gaussian support vector machine (SVM), 2) fine k-nearest neighbor (KNN) and 3) ensemble bagged model (EBM) trees were chosen for further evaluation on an independent test data set. The independent test data set consists of 115 COVID-19 CXR images collected from a local hospital and 100 CXR images collected from publically available data set containing normal lung and viral/bacterial pneumonia. Severity was scored between 0 to 4 by two experienced radiologists for each lung with pneumonia (both COVID-19 and non COVID-19) for the test data set. Ensemble Bagging Model Trees (EBM) with the selected radiomics features is the most suitable to distinguish between COVID-19 and other lung infections with an overall sensitivity of 87.8% and specificity of 97% (95.2% accuracy and 0.9228 area under curve) and is robust across severity levels. The method also can detect COVID-19 from CXR when two experienced radiologists were unable to detect any abnormality in the lung CXR (represented by severity score of 0). Once the CXR is acquired and lung is segmented, it takes less than two minutes for extracting radiomics features and providing diagnosis result. Since the proposed method does not require any manual intervention (e.g., sample collection etc.), it can be straightway integrated with standard X-ray reporting system to be used as an efficient, cost-effective and rapid early diagnosis device.

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

A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images.

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A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images.

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