Automated detection of COVID-19 cases using deep neural networks with X-ray images

By T. Ozturk, Muhammed Talo, E. Yildirim, Ulas Baran Baloglu, Özal Yıldırım, Usha R. Acharya. Comput. Biol. Medicine, 2020

2,261 citations recorded by the source corpus.

The novel coronavirus 2019 (COVID-2019), which first appeared in Wuhan city of China in December 2019, spread rapidly around the world and became a pandemic. It has caused a devastating effect on both daily lives, public health, and the global economy. It is critical to detect the positive cases as early as possible so as to prevent the further spread of this epidemic and to quickly treat affected patients. The need for auxiliary diagnostic tools has increased as there are no accurate automated toolkits available. Recent findings obtained using radiology imaging techniques suggest that such images contain salient information about the COVID-19 virus. Application of advanced artificial intelligence (AI) techniques coupled with radiological imaging can be helpful for the accurate detection of this disease, and can also be assistive to overcome the problem of a lack of specialized physicians in remote villages. In this study, a new model for automatic COVID-19 detection using raw chest X-ray images is presented. The proposed model is developed to provide accurate diagnostics for binary classification (COVID vs. No-Findings) and multi-class classification (COVID vs. No-Findings vs. Pneumonia). Our model produced a classification accuracy of 98.08% for binary classes and 87.02% for multi-class cases. The DarkNet model was used in our study as a classifier for the you only look once (YOLO) real time object detection system. We implemented 17 convolutional layers and introduced different filtering on each layer. Our model (available at (https://github.com/muhammedtalo/COVID-19)) can be employed to assist radiologists in validating their initial screening, and can also be employed via cloud to immediately screen patients.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/ebe3f062c05b57cb1f0f0f1e73ad23d8af6aef33

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In Artificial Intelligence

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images (2020, methodology): “Confusion matrix result of CoroNet on Dataset-2 [18] .”
A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images (2020, neutral): “The system developed in [58] obtained an overall accuracy of 98.”
Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model (2021, neutral): “Then, given the speciﬁed states, the transition probability matrix of states R ( m ) , m = 1 , was obtained per each indicator as follows: The tran siti on prob abil ity matr ix of stat es for the numb er of disc harg ed cases”
Deep learning based detection and analysis of COVID-19 on chest X-ray images (2020, background): “In [37], the authors proposed a model that automatically detects the Covid-19 with the help of Chest X-ray images.”
COVIDGR Dataset and COVID-SDNet Methodology for Predicting COVID-19 Based on Chest X-Ray Images (2020, neutral): “[20] No-Findings, COVID [8] + [14] DarkCovidNet 5-FCV 90.”
Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network (2020, methodology): “Ozturk et al. [43] propose an automated detection system for COVID-19 cases using deep neural networks and chest X-ray images.”
Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays (2020, positive): “Researchers in [30] consider a total of 1750 X-ray images with the DarkCovidNet deep learning model, in particular 250 COVID-19 positive, 500 related to other pulmonary pathologies and 10 0 0 obtained from healthy patients obtaining an accuracy of 0.98 and 0.87 evaluating two dataset.”
COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios (2020, positive): “[43] proposed a deep model for early detection of COVID-19 cases using X-ray images.”
Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks (2020, positive): “In this way, it is not necessary to use methods such as heatmap [52,54-56] to comprehend which part of image is effective in extracting features in CNN models.”
Automated Deep Transfer Learning-Based Approach for Detection of COVID-19 Infection in Chest X-rays (2020, positive): “We have used three class chest x-ray datasets obtained from [26].”