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COVID 19 Prediction Using Chest X-Ray And CNN Hyperparameter Optimizer
4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022 ; : 1586-1591, 2022.
Article in English | Scopus | ID: covidwho-2295522
ABSTRACT
According to mid-June 2020, the abrupt escalation of coronavirus reported widespread fear and crossed 16 million confirmed cases. To fight against this growth, clinical imaging is recommended, and for illustration, X-Ray images can be applied for opinion. This paper categorizes chest X-ray images into three classes- COVID-19 positive, normal, and pneumonia affected. We have used a CNN model for analysis, and hyperparameters are used to train and optimize the CNN layers. Swarm-based artificial intelligent algorithm - Grey Wolf Optimizer algorithm has been used for further analysis. We have tested our proposed methodology, and comparative analysis has been done with two openly accessible dataset containing COVID- 19 affected, pneumonia affected, and normal images. The optimized CNN model features delicacy, insight, values of F1 scores of 97.77, 97.74, 96.24 to 92.86, uniqueness, and perfection, which are better than models at the leading edge of technology. © 2022 IEEE.
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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Prognostic study Language: English Journal: 4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022 Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Type of study: Prognostic study Language: English Journal: 4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022 Year: 2022 Document Type: Article