CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images

By Asif Iqbal Khan, Junaid Latief Shah, M. Bhat. Comput. Methods Programs Biomed., 2020

1,161 citations recorded by the source corpus.

Background and Objective The novel Coronavirus also called COVID-19 originated in Wuhan, China in December 2019 and has now spread across the world. It has so far infected around 1.8 million people and claimed approximately 114,698 lives overall. As the number of cases are rapidly increasing, most of the countries are facing shortage of testing kits and resources. The limited quantity of testing kits and increasing number of daily cases encouraged us to come up with a Deep Learning model that can aid radiologists and clinicians in detecting COVID-19 cases using chest X-rays. Methods In this study, we propose CoroNet, a Deep Convolutional Neural Network model to automatically detect COVID-19 infection from chest X-ray images. The proposed model is based on Xception architecture pre-trained on ImageNet dataset and trained end-to-end on a dataset prepared by collecting COVID-19 and other chest pneumonia X-ray images from two different publically available databases. Results CoroNet has been trained and tested on the prepared dataset and the experimental results show that our proposed model achieved an overall accuracy of 89.6%, and more importantly the precision and recall rate for COVID-19 cases are 93% and 98.2% for 4-class cases (COVID vs Pneumonia bacterial vs pneumonia viral vs normal). For 3-class classification (COVID vs Pneumonia vs normal), the proposed model produced a classification accuracy of 95%. The preliminary results of this study look promising which can be further improved as more training data becomes available. Conclusion CoroNet achieved promising results on a small prepared dataset which indicates that given more data, the proposed model can achieve better results with minimum pre-processing of data. Overall, the proposed model substantially advances the current radiology based methodology and during COVID-19 pandemic, it can be very helpful tool for clinical practitioners and radiologists to aid them in diagnosis, quantification and follow-up of COVID-19 cases.

Subfields: Computer Vision and Pattern Recognition, Infectious Diseases, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/3a9b2a9a8127281b86e27ffd75170b93361fa78f

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In Computer Vision and Pattern Recognition

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Citations in the corpus

COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios (2020, neutral): “Khan et al. [42] the codes available and they claim that the it can be used to create a tool to assist radiologists in validating their initial screening.”
Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks (2020, methodology): “classified the chest X-ray images from normal, bacterial and viral pneumonia cases using the Xception architecture to detect COVID-19 infection [21].”
Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model (2021, neutral): “This hybrid model simultaneously utilizes the high and fast predictive power of the MLP model and the predictive power of the MC model based on the sequence of residual errors of the MLP model.”
CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection (2020, methodology): “Khan, Shah and Bhat [43] introduced a CNN architecture (CoroNet) to detect COVID-19 using CT and X-ray scans.”
Can AI Help in Screening Viral and COVID-19 Pneumonia? (2020, background): “Recently, several groups have reported deep machine learning techniques using X-ray images for detecting COVID-19 pneumonia [40]–[56].”
Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images (2021, positive): “[10] explored a limited number of machine learning algorithms for a four-class classification problem (COVID-19, bacterial pneumonia, viral pneumonia, and normal) with a very small dataset.”