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Deep CNN based Multi Classification of Respiratory Disease using X-Ray Images
3rd International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2285235
ABSTRACT
COVID-19 debuted in Wuhan, China on December 19, 2019. In a brief period, deadly virus now migrated to practically every country. To avoid the causative agent COVID-19 disease, governments implement a number of strict restrictions, notably prohibiting people from leaving their homes. This paper focused on detecting and classifying disease such as viral pneu-monia, covidand normal from x-ray images using deep learning methods along with pre-trained models. Moreover, validation accuracy of CNN model attained around 91 % while performing layers in neural network. Several investigations examined that identifying disease of covid reached more accuracy around 98% with hybrid and other algorithms without removing noise from particular images. But this work mainly focused on normalizing images to make the computation very efficient, convergence faster too. © 2022 IEEE.
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 3rd International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2022 Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 3rd International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2022 Year: 2022 Document Type: Article