Detection of COVID-19 cases through X-ray images using hybrid deep neural network

Abstract

Purpose The latest 2019 coronavirus (COVID-2019), which first appeared in December 2019 in Wuhan's city in China, rapidly spread around the world and became a pandemic. It has had a devastating impact on daily lives, the public's health and the global economy. The positive cases must be identified as soon as possible to avoid further dissemination of this disease and swift care of patients affected. The need for supportive diagnostic instruments increased, as no specific automated toolkits are available. The latest results from radiology imaging techniques indicate that these photos provide valuable details on the virus COVID-19. User advanced artificial intelligence (AI) technologies and radiological imagery can help diagnose this condition accurately and help resolve the lack of specialist doctors in isolated areas. In this research, a new paradigm for automatic detection of COVID-19 with bare chest X-ray images is displayed. Images are presented. The proposed model DarkCovidNet is designed to provide correct binary classification diagnostics (COVID vs no detection) and multi-class (COVID vs no results vs pneumonia) classification. The implemented model computed the average precision for the binary and multi-class classification of 98.46% and 91.352%, respectively, and an average accuracy of 98.97% and 87.868%. The DarkNet model was used in this research as a classifier for a real-time object detection method only once. A total of 17 convolutionary layers and different filters on each layer have been implemented. This platform can be used by the radiologists to verify their initial application screening and can also be used for screening patients through the cloud. Design/methodology/approach This study also uses the CNN-based model named Darknet-19 model, and this model will act as a platform for the real-time object detection system. The architecture of this system is designed in such a way that they can be able to detect real-time objects. This study has developed the DarkCovidNet model based on Darknet architecture with few layers and filters. So before discussing the DarkCovidNet model, look at the concept of Darknet architecture with their functionality. Typically, the DarkNet architecture consists of 5 pool layers though the max pool and 19 convolution layers. Assume as a convolution layer, and as a pooling layer. Findings The work discussed in this paper is used to diagnose the various radiology images and to develop a model that can accurately predict or classify the disease. The data set used in this work is the images bases on COVID-19 and non-COVID-19 taken from the various sources. The deep learning model named DarkCovidNet is applied to the data set, and these have shown signification performance in the case of binary classification and multi-class classification. During the multi-class classification, the model has shown an average accuracy 98.97% for the detection of COVID-19, whereas in a multi-class classification model has achieved an average accuracy of 87.868% during the classification of COVID-19, no detection and Pneumonia. Research limitations/implications One of the significant limitations of this work is that a limited number of chest X-ray images were used. It is observed that patients related to COVID-19 are increasing rapidly. In the future, the model on the larger data set which can be generated from the local hospitals will be implemented, and how the model is performing on the same will be checked. Originality/value Deep learning technology has made significant changes in the field of AI by generating good results, especially in pattern recognition. A conventional CNN structure includes a convolution layer that extracts characteristics from the input using the filters it applies, a pooling layer that reduces calculation efficiency and the neural network's completely connected layer. A CNN model is created by integrating one or more of these layers, and its internal parameters are modified to accomplish a specific mission, such as classification or object recognition. A typical CNN structure has a convolution layer that extracts features from the input with the filters it applies, a pooling layer to reduce the size for computational performance and a fully connected layer, which is a neural network. A CNN model is created by combining one or more such layers, and its internal parameters are adjusted to accomplish a particular task, such as classification or object recognition.

Detail-Oriented Capsule Network for classification of CT scan images performing the detection of COVID-19

“…Diao et al [20] has shown the effect of COVID-19 on the kidney and acute renal failure. Nair et al [21] have built a CNN model on more levels and modified its internal parameters for a specific task such as classification or recognizing objects. There is a typical CNN structure with an extract filter from the input by a convolution layer.…”

Outbreak prediction of COVID-19 using Recurrent neural network with Gated Recurrent Units

“…This puts government officials in unexpected situations. Non-pharmaceutical interventions, like the use of effective face masks, the closure of educational institutions, the restriction of travel, and stringent containment measures, have the most significant interventions for flattening the Epidemic Curve [3] . Furthermore, mass testing and tracking are also essential if the continuous transmission chain is to be broken.…”

“…That lack of interest encourages people to take impulsive act, forcing government ministers in new situations. Non-pharmaceutical interventions with effective face covering, the lockdown of academic institutions, restricted transportation, and additional security are the most powerful interventional tactics to smooth the Pandemic Slope [3] , [4] , [5] . This concludes that if the continuous transmission chain is to be disrupted, then widespread testing and tracking are required.…”

Role of Hybrid Deep Neural Networks (HDNNs), Computed Tomography, and Chest X-rays for the Detection of COVID-19

“…Recently, neural networks gained excessive achievements in the field of medical imaging, due to their self-learning capabilities and high aptitude for automatic feature extraction [16]. Especially, deep neural networks can distinguish infectious and virusrelated pneumonia for chest radiographs [17][18][19][20][21]. Therefore, in this article, we introduce a hybrid deep neural network (HDDNs) for the diagnosis of COVID-19, using CT and X-ray images.…”