Covid-19 Diagnosis: Comparative Approach Between Chest X-Ray and Blood Test Data
6th International Conference on Computer Science and Engineering, UBMK 2021
; : 472-477, 2021.
Article
in English
| Scopus | ID: covidwho-1741302
ABSTRACT
The Covid-19 virus has made a major impact on the world and is still spreading rapidly. A reliable solution to prevent further damage, early diagnosis of coronavirus patients are incredibly important. While chest X-Ray diagnosis is the easiest and fastest solution for this, an average radiologist has only a 75% to 85% accuracy when evaluating X-Ray data, thus it is desirable to achieve an accurate artificial network for this. Throughout this study, chest X-Ray data and blood routine test data are utilised and compared. X-Ray data consists of 5000 chest X-Ray images which are gathered from an open-source research and from a local hospital in which both have anonymous data. The blood test results were also taken from the same hospital. For the chest X-Ray diagnosis we utilised two of the popular convolutional neural networks, which are Resnet18 and Squeezenet and concluded that Resnet18 provided slightly more accurate results, while both having almost 98% accuracy. For blood test diagnosis, a feed-forward multi layer neural network was used. Even though it was worked on an insufficient dataset, 72% accuracy was obtained, thus making it a feasible option for further research. Hence, we concluded that in general chest X-Ray diagnosis is preferable over routine blood test diagnosis and the usage of AI yields better approximate results than humans. © 2021 IEEE
Blood test; Chest x-ray; COVID-19 diagnosis; Supervised learning; Transfer learning; Blood; Convolutional neural networks; Hospitals; Multilayer neural networks; Network layers; Viruses; Chest x-rays; Comparative approach; COVID-19 diagnose; Early diagnosis; Test data; Test diagnosis; X ray data; X-ray diagnosis; Diagnosis
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
6th International Conference on Computer Science and Engineering, UBMK 2021
Year:
2021
Document Type:
Article
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