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Deep Learning Frameworks for COVID-19 Detection
3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021 ; : 1048-1053, 2021.
Article in English | Scopus | ID: covidwho-1476057
ABSTRACT
The COVID-19 (previously known as '2019 novel coronavirus') took the big form and outspread rapidly around the world becoming a pandemic. Artificial intelligence tools come out to be one of the fastest solutions to detect the disease and in another way helping to control the spread. This paper signifies how chest X-ray images use deep learning techniques which are very useful for analyzing images to detect the virus and spotting high-risk patients for controlling the spread. Further, it shows how the Convolutional Neural Network (CNN) technology of deep learning helps to detect the virus quickly. A CNN is a type of artificial neural network that is used for image pre-processing and consists of many layers that aid in detection. A sequential CNN model is proposed with different kernel sizes, filters, and having different parameters using a dataset of 2159 images. The output shows that a model with an adequate amount of filters, max-pooling layers, dropout layers and dense layers imparts the highest accuracy of 99.53% in detecting the coronavirus. © 2021 IEEE.

Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Diagnostic study Language: English Journal: 3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021 Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Diagnostic study Language: English Journal: 3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021 Year: 2021 Document Type: Article