Detection of Coronavirus Disease (COVID-19) Based on Deep Features

Sethy, Prabira Kumar; Behera, Santi Kumari

doi:10.20944/preprints202003.0300.v1

Cited by 533 publications

(437 citation statements)

References 25 publications

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“…In [3], a modified version of ResNet-50 pre-trained network has been provided to classify CT images into three classes: healthy, COVID-19 and bacterial pneumonia. Chest x-ray images (CXR) were used in [4] by a CNN constructed based on various ImageNet pre-trained models to extract the high level features. Those features were fed into a Support Vector Machine SVM as a machine learning classifier in order to detect the COVID-19 cases.…”

Section: Introductionmentioning

confidence: 99%

Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

Abbas

Abdelsamea

Gaber

2020

Preprint

388

338

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Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural networks (CNN s) for image recognition and classification. However, due to the limited availability of annotated medical images, the classification of medical images remains the biggest challenge in medical diagnosis. Thanks to transfer learning, an effective mechanism that can provide a promising solution by transferring knowledge from generic object recognition tasks to domain-specific tasks. In this paper, we validate and adopt our previously developed CNN, called Decompose, Transfer, and Compose (DeTraC ), for the classification of COVID-19 chest X-ray images. DeTraC can deal with any irregularities in the image dataset by investigating its class boundaries using a class decomposition mechanism. The experimental results showed the capability of DeTraC in the detection of COVID-19 cases from a comprehensive image dataset collected from several hospitals around the world. High accuracy of 95.12% (with a sensitivity of 97.91%, a specificity of 91.87%, and a precision of 93.36%) was achieved by DeTraC in the detection of COVID-19 X-ray images from normal, and severe acute respiratory syndrome cases.

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Section: Introductionmentioning

confidence: 99%

Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

Abbas

Abdelsamea

Gaber

2020

Preprint

388

338

View full text Add to dashboard Cite

show abstract

“…The average accuracy found was 87.66%. [45] also investigated radiographs as a diagnostic method for Covid-19. They organized two databases: the first, with 25 positive and 25 negative images for Covid-19 (or pneumonia); the second base, included MERS, SARS and ARDS in the Covid-19 positive class, with a total of 266 images.…”

Section: Related Workmentioning

confidence: 99%

IKONOS: An intelligent tool to support diagnosis of Covid-19 by texture analysis of x-ray images

Gomes

Barbosa

Santana

et al. 2020

Preprint

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In late 2019, the SARS-Cov-2 spread worldwide. The virus has high rates of proliferation and causes severe respiratory symptoms, such as pneumonia. There is still no specific treatment and diagnosis for the disease. The standard diagnostic method for pneumonia is chest X-ray image. There are many advantages to using Covid-19 diagnostic X-rays: low cost, fast and widely available. We propose an intelligent system to support diagnosis by X-ray images. We tested Haralick and Zernike moments for feature extraction. Experiments with classic classifiers were done. Support vector machines stood out, reaching an average accuracy of 89.78%, average recall and sensitivity of 0.8979, and average precision and specificity of 0.8985 and 0.9963 respectively. The system is able to differentiate Covid-19 from viral and bacterial pneumonia, with low computational cost.

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“…In another study, Sethy et al 21 used chest x-ray images to recognize COVID viral infection, firstly they have extracted deep features using CNN based on pre-trained ImageNETand at the last layer SVM were used in order to classify it. In addition, Wang et al 16 presented multi-class classification, deep convolutional neural network architecture named as COVID-Net implemented over 16, 756 chest radiography images that have been scanned with 13,645 patient to classify the COVID-19 and non-COVID also with a safe and bacterial infected patient.…”

Section: Introductionmentioning

confidence: 99%

Accurate Prediction of COVID-19 using Chest X-Ray Images through Deep Feature Learning model with SMOTE and Machine Learning Classifiers

Kumar

Arora

Bansal

et al. 2020

Preprint

116

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According to the World Health Organization (WHO), the coronavirus (COVID-19) pandemic is putting even the best healthcare systems across the world under tremendous pressure. The early detection of this type of virus will help in relieving the pressure of the healthcare systems. Chest X-rays has been playing a crucial role in the diagnosis of diseases like Pneumonia. As COVID-19 is a type of influenza, it is possible to diagnose using this imaging technique. With rapid development in the area of Machine Learning (ML) and Deep learning, there had been intelligent systems to classify between Pneumonia and Normal patients. This paper proposes the machine learning-based classification of the extracted deep feature using ResNet152 with COVID-19 and Pneumonia patients on chest X-ray images. SMOTE is used for balancing the imbalanced data points of COVID-19 and Normal patients. This non-invasive and early prediction of novel coronavirus (COVID-19) by analyzing chest X-rays can further be used to predict the spread of the virus in asymptomatic patients. The model is achieving an accuracy of 0.973 on Random Forest and 0.977 using XGBoost predictive classifiers. The establishment of such an approach will be useful to predict the outbreak early, which in turn can aid to control it effectively.

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Detection of Coronavirus Disease (COVID-19) Based on Deep Features

Cited by 533 publications

References 25 publications

Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

IKONOS: An intelligent tool to support diagnosis of Covid-19 by texture analysis of x-ray images

Accurate Prediction of COVID-19 using Chest X-Ray Images through Deep Feature Learning model with SMOTE and Machine Learning Classifiers

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