3d8d13581898e639443-5687 20230825082647255 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 08 30 2023 12 9 Research Scholar, Department of Instrumentation and Control, Gujarat Technological University, Ahmedabad (Gujarat), India. Bhautik Daxini https://orcid.org/0009-0000-2760-3248 Dr. M.K. Shah https://orcid.org/0009-0001-6167-0257 Associate Prof. & Head, Department of Instrumentation & Control Engineering, Vishwakarma Government Engineering College, Chandkheda, (Gujarat), India. Rutvik K. Shukla https://orcid.org/0000-0001-5223-8465 Assistant Prof., Department of Instrumentation & Control Engineering, Government Engineering College, Rajkot (Gujarat), India. Dr. Rohit Thanki https://orcid.org/0000-0002-0645-6266 Data Scientist, KRiAN GmbH, Wolfsburg, Germany. Viral Thakar Senior Machine Learning Engineer, Autodesk, Toronto, Ontario, Canada. Deep learning methodologies are now feasible in practically every sphere of modern life because to technological advancements. Because of its high level of accuracy, deep learning can automatically diagnose and classify a wide variety of medical conditions in the field of medicine. The coronavirus first appeared in Wuhan, China, in December 2019, and quickly spread throughout the world. The pandemic of COVID-19 presented significant challenges to the world's health care system. PCR and medical imaging can diagnose COVID-19. There has a negative impact on the health of people as well as the global economy, education, and social life. The most significant challenge in stymieing the rapid propagation of the disease is locating positive Corona patients as promptly as possible. Because there are no automated tool kits, additional diagnostic equipment will be required. According to radiological studies, these images include important information about the coronavirus. Accurate treatment of this virus and a solution to the problem of a lack of medical professionals in remote areas may be possible with the help of a specialized Artificial Intelligence (AI) system and radiographic pictures. We used pre-trained CNN models Xception, Inception, ResNet-50, ResNet-50V2, DenseNet121, and MobileNetV2 to correct the COVID-19 classification analytics. In this paper, we investigate COVID-19 detection methods that make use of chest X-rays. According to the findings of our research, the pre-trained CNN Model that makes use of MobileNetV2 performs better than other CNN techniques in terms of both the size of the solution and its speed. Our method might be of use to researchers in the process of fine-tuning the CNN model for efficient COVID screening. 08 30 2023 1 16 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true International Journal of Innovative Technology and Exploring Engineering (IJITEE) No, I did not receive it. No conflicts of interest to the best of our knowledge. No, the article does not require ethical approval and consent to participate with evidence. Not relevant. All authors have equal participation in this article. 10.35940/ijitee.I9696.0812923 https://www.ijitee.org/portfolio-item/I96960812923/