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• W4386174822 abstract "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." @default.
• W4386174822 created "2023-08-26" @default.
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• W4386174822 date "2023-08-30" @default.
• W4386174822 modified "2023-10-16" @default.
• W4386174822 title "A Comparative Evaluation of Diverse Deep Learning Models for the COVID-19 Prediction" @default.
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• W4386174822 doi "https://doi.org/10.35940/ijitee.i9696.0812923" @default.
• W4386174822 hasPublicationYear "2023" @default.
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