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Automatic classification between COVID-19 pneumonia, lung cancer and normal lung tissues on chest CT Scans
6th International Conference on Advances in Biomedical Engineering (ICABME) ; : 197-201, 2021.
Article in English | Web of Science | ID: covidwho-1822023
ABSTRACT
Coronavirus sickness (COVID-19) may be a pandemic sickness, that has already caused thousands of casualties and infected many countless individuals worldwide. Whereas most of the individuals infected with the COVID-19 intimate with delicate to moderate respiratory disease, some developed deadly respiratory illness. Any technological tool sanctioning screening of the COVID-19 infection with high accuracy will be crucially useful to the attention professionals. The usage of chest CT scan pictures for classifying and diagnosing COVID-19 respiratory illness has shown an excellent range of exactness and accuracy quite the other tool that lessens the number of deaths within the severe cases. This paper presents a proposed model of convolutional neural network (CNN) with a large multi-national dataset that is able to classify covid-19 pneumonia;lung cancer and the normal lung tissues from chest computed tomography (CT) scans with a classification accuracy of 94.05%.
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Full text: Available Collection: Databases of international organizations Database: Web of Science Language: English Journal: 6th International Conference on Advances in Biomedical Engineering (ICABME) Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Web of Science Language: English Journal: 6th International Conference on Advances in Biomedical Engineering (ICABME) Year: 2021 Document Type: Article