COVID Faster R-CNN: A Novel Framework to Diagnose Novel Coronavirus Disease (COVID-19) in X-Ray Images

Abstract

COVID-19 or novel coronavirus disease, which has already been declared as a worldwide pandemic, at first had an outbreak in a small town of China, named Wuhan. More than two hundred countries around the world have already been affected by this severe virus as it spreads by human interaction. Moreover, the symptoms of novel coronavirus are quite similar to the general flu. Screening of infected patients is considered as a critical step in the fight against COVID-19. Therefore, it is highly relevant to recognize positive cases as early as possible to avoid further spreading of this epidemic. However, there are several methods to detect COVID-19 positive patients, which are typically performed based on respiratory samples and among them one of the critical approach which is treated as radiology imaging or X-Ray imaging. Recent findings from X-Ray imaging techniques suggest that such images contain relevant information about the SARS-CoV-2 virus. In this article, we have introduced a Deep Neural Network (DNN) based Faster Regions with Convolutional Neural Networks (Faster R-CNN) framework to detect COVID-19 patients from chest X-Ray images using available open-source dataset. Our proposed approach provides a classification accuracy of 97.36%, 97.65% of sensitivity, and a precision of 99.28%. Therefore, we believe this proposed method might be of assistance for health professionals to validate their initial assessment towards COVID-19 patients.

Applications of artificial intelligence in battling against covid-19: A literature review

“…Other examples of the research that use CNN for detecting covid-19 via CT images include [295] , [296] , [297] , [298] , [299] , [300] , [301] , [302] , [303] , [304] , [305] , [306] , [307] , [308] , [309] , [310] .…”

COVID-19 Detection by Optimizing Deep Residual Features with Improved Clustering-Based Golden Ratio Optimizer

“…Again, K.H. Shibly et al [27] proposed a technique, named faster R-CNN, to detect COVID-19 from X-ray images. They have implemented their model on two publicly available datasets, one is a customized dataset and another one is COVIDx, and obtained an accuracy of 97.36% and 97.65%.…”

Artificial intelligence and COVID-19: Present state and future vision

“…Deep learning architectures included visual geometry group (VGG)-16 or VGG-19, Resnet 50, Xception, DenseNet201, Inception_ResNet_V2 and Inception_V3 with data sources that ranged from Kaggle, GitHub, and various hospitals, especially from cities in China. Impressive variations of CNN for medical imaging included: combined CNN-LSTM network ( 6 ), faster regions with CNN ( 7 ), and a hybrid VGG-based neural network and data augmentation and spatial transformer network (STN) with CNN (VDSNet)( 8 ). There were also reports of using synthetic data from generative adversarial networks (GANs)( 9 ).…”