Image recognition of COVID-19 using DarkCovidNet architecture based on convolutional neural network
| Autor: | Deepak Omprakash Gupta, SC Vimal, Dinesh C. Jain, P. Pavan Kumar, Bhavna Bajpai |
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| Rok vydání: | 2021 |
| Předmět: |
2019-20 coronavirus outbreak
medicine.diagnostic_test Coronavirus disease 2019 (COVID-19) Computer science business.industry Mechanical Engineering Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Computed tomography Geotechnical Engineering and Engineering Geology Convolutional neural network Cross entropy Mechanics of Materials medicine Computer vision Artificial intelligence Electrical and Electronic Engineering Architecture business Focus (optics) Civil and Structural Engineering |
| Zdroj: | World Journal of Engineering. 19:90-97 |
| ISSN: | 1708-5284 |
| Popis: | Purpose The purpose of this study/paper To focus on finding COVID-19 with the help of DarkCovidNet architecture on patient images. Design/methodology/approach We used machine learning techniques with convolutional neural network. Findings Detecting COVID-19 symptoms from patient CT scan images. Originality/value This paper contains a new architecture for detecting COVID-19 symptoms from patient computed tomography scan images. |
| Databáze: | OpenAIRE |
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