COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images

By Ezz El-Din Hemdan, Marwa A. Shouman, Mohamed Esmail Karar. arXiv.org, 2020

1,074 citations recorded by the source corpus.

Background and Purpose: Coronaviruses (CoV) are perilous viruses that may cause Severe Acute Respiratory Syndrome (SARS-CoV), Middle East Respiratory Syndrome (MERS-CoV). The novel 2019 Coronavirus disease (COVID-19) was discovered as a novel disease pneumonia in the city of Wuhan, China at the end of 2019. Now, it becomes a Coronavirus outbreak around the world, the number of infected people and deaths are increasing rapidly every day according to the updated reports of the World Health Organization (WHO). Therefore, the aim of this article is to introduce a new deep learning framework; namely COVIDX-Net to assist radiologists to automatically diagnose COVID-19 in X-ray images. Materials and Methods: Due to the lack of public COVID-19 datasets, the study is validated on 50 Chest X-ray images with 25 confirmed positive COVID-19 cases. The COVIDX-Net includes seven different architectures of deep convolutional neural network models, such as modified Visual Geometry Group Network (VGG19) and the second version of Google MobileNet. Each deep neural network model is able to analyze the normalized intensities of the X-ray image to classify the patient status either negative or positive COVID-19 case. Results: Experiments and evaluation of the COVIDX-Net have been successfully done based on 80-20% of X-ray images for the model training and testing phases, respectively. The VGG19 and Dense Convolutional Network (DenseNet) models showed a good and similar performance of automated COVID-19 classification with f1-scores of 0.89 and 0.91 for normal and COVID-19, respectively. Conclusions: This study demonstrated the useful application of deep learning models to classify COVID-19 in X-ray images based on the proposed COVIDX-Net framework. Clinical studies are the next milestone of this research work.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/d37234b062b448966288c1b369b6af8f0e073979

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Citations in the corpus

Coronavirus (Covid-19) Classification Using CT Images by Machine Learning Methods (2020, result): “There are also some studies, which used lung X-ray scans [11-14].”
Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks (2020, positive): “Similarly, Hemdan et al. [31] proposed a deep learning model for the diagnosis of COVID-19 disease, and compared the model with 7 different deep learning algorithms.”
Deep Transfer Learning Based Classification Model for COVID-19 Disease (2020, neutral): “persons from their chest CT images [1].”
Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model (2021, neutral): “However, the difference between these two models is very slight, which indicates that the MLP model, which is a simpler model, can also be used alone to increase the forecast speed and reduce the calculations.”
Using X-ray images and deep learning for automated detection of coronavirus disease (2020, background): “In Hemdan et al. (2020), a deep learning classifiers framework “ COVIDX-Net ” helping radiologists to automatically identify Covid19 was proposed.”
Deep learning approaches for COVID-19 detection based on chest X-ray images (2020, neutral): “Hemdan, Shouman, and Karar (2020) developed COVIDX-Net for the automated detection of COVID-19 using chest X-ray images, obtaining a COVID-19 classification accuracy of 91%.”
COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data (2020, methodology): “A range of deep learning frameworks coined as COVIDX-Net trained on a small data set of 25 conﬁrmed COVID-19 cases [55].”
A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images (2020, methodology): “[30] introduced a deep learning technique to detect COVID-19 using X-ray images.”
Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning Based Approach (2020, neutral): “[13] developed a deep learning framework, COVIDX-Net, to diagnose COVID-19 in X-Ray Images.”
Automated detection of COVID-19 cases using deep neural networks with X-ray images (2020, methodology): “Few studies conducted prior to this study have used 25 and 50 images in each class [41,42,44,45].”
Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks (2020, methodology): “used VGG19 and DenseNet models to diagnose COVID-19 from X-ray images [24].”
Automated Deep Transfer Learning-Based Approach for Detection of COVID-19 Infection in Chest X-rays (2020, neutral): “An activate function is then used as [31–33]: Here, R l deﬁnes latent demonstration of k th feature of the layer.”