• KUZHALYN YASHVI T T
• BALASUBRAMANI D
• MANOJ KUMAR J
• K M KIRTHIKA

A novel coronavirus (COVID-19) has emerged recently like an acute respiratory syndrome, causing a worldwide pneumonia outbreak. As the COVID-19 virus infects rapidly across the globe, computed tomography (CT) has become crucial for accurate detection. The COVID-19 diagnostic approach is split into two parts: laboratory-based diagnostics and chest radiography diagnostics. The number of studies using artificial intelligence (AI) techniques to diagnose COVID-19 with chest computed tomography has rapidly increased in recent months (CT). We reviewed the diagnosis of COVID-19 using chest CT toward AI in this study. The purpose of this study is to see if self-supervision improves classification performance on a dataset of COVID-19 CT scans. The experimental study was conducted based on the data in order to compare the classification performance of the proposed method of self-supervision with various amounts of data. The experimental results demonstrate nearly 6% increase in accuracy with self-supervision compared to no self-supervision on average, and a more than 8% increase in accuracy in our best run with self-supervision compared to no self-supervision. According to the findings, self-supervision can improve classification performance on a small COVID-19 CT scan dataset.

Covid-19, Classification, Chest Computed Tomography (CT) images, Convolutional Neural Network.

"PREDICTION OF COVID-19 FROM CT IMAGES USING SVM APPROACH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 5, page no.c496-c499, May-2022, Available :http://www.jetir.org/papers/JETIR2205270.pdf

"PREDICTION OF COVID-19 FROM CT IMAGES USING SVM APPROACH", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 5, page no. ppc496-c499, May-2022, Available at : http://www.jetir.org/papers/JETIR2205270.pdf