An Intelligent Information System and Application for the Diagnosis and Analysis of COVID-19

Document Type

Book Chapter

Source of Publication

Lecture Notes in Networks and Systems

Publication Date

1-1-2022

Abstract

The novel coronavirus spread across the world at the start of 2020. Millions of people are infected due to the COVID-19. At the start, the availability of corona test kits is challenging. Researchers analyzed the current situation and produced the COVID-19 detection system on X-ray scans. Artificial intelligence (AI) based systems produce better results in terms of COVID detection. Due to the overfitting issue, many AI-based models cannot produce the best results, directly impacting model performance. In this study, we also introduced the CNN-based technique for classifying normal, pneumonia, and COVID-19. In the proposed model, we used batch normalization to regularize the mode land achieve promising results for the three binary classes. The proposed model produces 96.56% accuracy for the classification for COVID-19 vs. Normal. Finally, we compared our model with other deep learning-based approaches and discovered that our approach outperformed.

Publisher

Springer Nature

Volume

371

Disciplines

Computer Sciences | Medicine and Health Sciences

Keywords

COVID-19, CNN, Batch normalization, Classification

Scopus ID

85122530730

Indexed in Scopus

yes

Open Access

no

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