Deep Residual Neural Network for COVID-19 Detection from Chest X-ray Images

Abstract

The COVID-19 diffused quickly throughout the world and converted as a pandemic. It has caused a destructive effect on both regular lives, common health and global business. It is crucial to identify positive patients as shortly as desirable to limit this epidemic’s further diffusion and to manage immediately affected cases. The demand for quick assistant distinguishing devices has developed. Recent findings achieved utilizing radiology imaging systems propose that such images include salient data about the COVID-19. The utilization of progressive artificial intelligence (AI) methods linked by radiological imaging can help the reliable diagnosis of COVID-19. As radiography images can recognize pneumonia infections, this research brings an accurate and automatic technique based on a deep residual network to analyze chest X-ray images to monitor COVID-19 and diagnose verified patients. The physician states that it is significantly challenging to separate COVID-19 from common viral and bacterial pneumonia, while COVID-19 is additionally a variety of viruses. The proposed network is expanded to perform detailed diagnostics for two multi-class classification (COVID-19, Normal, Viral Pneumonia) and (COVID-19, Normal, Viral Pneumonia, Bacterial Pneumonia) and binary classification. By comparing the proposed network with the popular methods on public databases, the results show that the proposed algorithm can provide an accuracy of 92.1% in classifying multi-classes of COVID-19, normal, viral pneumonia, and bacterial pneumonia cases. It can be applied to support radiologists in verifying their first viewpoint.

The Possibilities of Prospective Assessment of SARS-CoV-2 Infection Risk Based on an Artificial Neural Network Model – A Cross-sectional and In Silico Study.

“…between June 2020 and P r e p r i n t 10 August 2022, which, combined with the dynamically changing epidemiological situation and related changing recommendations, the tool may require updates in the future, which is the purpose of the already planned follow up study. Also, the number of cases used to teach the network may have been a limitation; publicly available networks for identifying images like ResNet-50 (23) and GoogLeNet (24) were trained on tens of thousands of cases, significantly increasing their predictive capabilities (25). possible (17)(18)(19).…”

Application of machine learning in the fight against the COVID-19 pandemic: A review

“…Other MLbased systems for COVID-19 diagnosis usually use CT scan readings. For example, neural networkbased systems make it possible to distinguish COVID-19 symptoms from other lung diseases (10), (43), (44).…”

SCovNet: A skip connection-based feature union deep learning technique with statistical approach analysis for the detection of COVID-19

“…As of now, professionals from all across the world are working hard to fight against the disease. Many researchers and academicians published different articles describing methods for detecting COVID-19 using CXR images [18] , [19] , [20] , [21] , [22] . Using image processing on chest X-ray images, Hasoon et al suggested a method for classifying and early detecting COVID-19.…”