Varalakshmi, Sudha and P, Vijayalakshmi. and V, Rajendran. (2022) Deep CNN based Multi Classification of Respiratory Disease using X-Ray Images. In: 2022 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), Chennai, India.
Full text not available from this repository. (Request a copy)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.
Item Type: | Conference or Workshop Item (Paper) |
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Subjects: | Electronics and Communication Engineering > Fiber-Optic Communication |
Divisions: | Electronics and Communication Engineering |
Depositing User: | Mr IR Admin |
Date Deposited: | 14 Sep 2024 06:50 |
Last Modified: | 14 Sep 2024 06:50 |
URI: | https://ir.vistas.ac.in/id/eprint/6033 |