Classification of COVID-19 using Chest X-ray Images with Deep Learning Techniques-CNN & ResNet-18

-74813b3e17f460286df-2df0 20220507030947064 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 05 30 2022 11 6 Classification of COVID-19 using Chest X-ray Images with Deep Learning Techniques-CNN & ResNet-18 Department of Computer Science and Engineering, Gitam University, Visakhapatnam (A.P), India. Kotra Sai Kirthana To classify the covid-19 images as infectious or normal, it has been evident that the chest X-ray is a powerful tool to diagnose due to its crucial characteristics of convenience, inexpensiveness and rapid pace. The work aims to determine covid-19 in the infected patients by training models with the dataset using convolutional neural networks (CNN) and ResNet-18 and to draw comparisons in their performances respectively. To handle the dataset by applying various operations to simplify and to make it ready for training, validation and testing procedures of both the algorithms involved. The accuracies obtained on testing CNN and RESNET-18 models are 96.07% and 96.67% respectively. Hence the objective of the work is achieved and the results are obtained by implementing covid-19 classification using chest x-ray images with CNN and resnet-18 models. 05 30 2022 49 52 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.F9922.0511622 https://www.ijitee.org/portfolio-item/f99220511622/