Publication Type : Conference Paper
Publisher : Springer, Cham
Source : Communications in Computer and Information Science
Url : https://doi.org/10.1007/978-3-031-12641-3_5
Campus : Amritapuri
School : School of Engineering
Department : Electronics and Communication
Year : 2022
Abstract : COVID-19, a disease caused by corona virus is a worldwide pandemic which put millions into death. Not only on lives of people but also it has affected all countries in terms of development and wealth. The main challenge is to detect COVID-19 effectively with high accuracy. A fast classification algorithm can help the health professionals in many ways. This work focuses on the implementation analysis of various Convolutional Neural Network (CNN) which are pre-trained in detecting the disease from chest X-ray images with highest accuracy. Transfer learning is utilized, and fine tuning is performed to get a reliable classification of the image data. For the pre-trained CNN model Mobilenet-V2, highest accuracy of 94.298% and precision of 89.76% obtained.
Cite this Research Publication : Nair, A.R., Remya Ajai, A.S. (2022). Analysis of COVID-19 Detection Algorithms Based on Convolutional Neural Network Models Using Chest X-ray Images. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2022. Communications in Computer and Information Science, vol 1614. Springer, Cham. https://doi.org/10.1007/978-3-031-12641-3_5