Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks

By Ali Narin, Ceren Kaya, Ziynet Pamuk. 2020

1,859 citations recorded by the source corpus.

The 2019 novel coronavirus disease (COVID-19), with a starting point in China, has spread rapidly among people living in other countries and is approaching approximately 101,917,147 cases worldwide according to the statistics of World Health Organization. There are a limited number of COVID-19 test kits available in hospitals due to the increasing cases daily. Therefore, it is necessary to implement an automatic detection system as a quick alternative diagnosis option to prevent COVID-19 spreading among people. In this study, five pre-trained convolutional neural network-based models (ResNet50, ResNet101, ResNet152, InceptionV3 and Inception-ResNetV2) have been proposed for the detection of coronavirus pneumonia-infected patient using chest X-ray radiographs. We have implemented three different binary classifications with four classes (COVID-19, normal (healthy), viral pneumonia and bacterial pneumonia) by using five-fold cross-validation. Considering the performance results obtained, it has been seen that the pre-trained ResNet50 model provides the highest classification performance (96.1% accuracy for Dataset-1, 99.5% accuracy for Dataset-2 and 99.7% accuracy for Dataset-3) among other four used models.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks (2020, neutral): “Narin et al. [32] performed COVID-19 diagnosis using chest X-ray images by developing the ResNet50, InceptionV3, and Inception-ResNetv2 deep learning models.”
COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios (2020, neutral): “[38] evaluated COVID-19 detection on CXR images using three different deep neural network models (i.”
Improving performance of CNN to predict likelihood of COVID-19 using chest X-ray images with preprocessing algorithms (2020, background): “In these two imaging modalities, although CT can achieve higher sensitivity read by radiologists, chest X-ray radiography has clinical application advantages including low cost, low radiation dose, easy-to-operate and wide accessibility in general or community hospitals [2].”
Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model (2021, neutral): “This hybrid model can be seen as a general forecast model, as it can be applied to other time periods, other places, and other indicators related to Covid-19 or other areas.”
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].”
CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection (2020, methodology): “Narin, Kaya and Pamuk [45] applied COVID-19 diagnosis using chest X-ray images by developing the ResNet50, Inception-ResNetv2 and InceptionV3 deep learning models.”
Deep learning based detection and analysis of COVID-19 on chest X-ray images (2020, background): “Every day, people lose their life due to covid-19 [8], and the diagnostic cost of this disease is very high in the context of a country, state, and patients [9].”
AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system (2020, neutral): “Narin et al. [42] develop three widely-used models, i.e., ResNet-50 [43], Inception-V3 [44], and Inception-ResNet-V2 [45], to detect COVID-19 lesion in X-ray images and among them ResNet-50 achieves the best classification performance.”
Extracting Possibly Representative COVID-19 Biomarkers from X-ray Images with Deep Learning Approach and Image Data Related to Pulmonary Diseases (2020, result): “Recent studies claim to achieve remarkable results regarding the automatic detection of the disease from thoracic X-Ray scans [1-3].”
Machine-Learning-Based Disease Diagnosis: A Comprehensive Review (2021, methodology): “Several studies investigated and advised screening COVID-19 patients utilizing chest X-ray images in parallel, with major contributions in [95, 96, 97].”