Looking at the global pandemic that had a huge impact on human lives since the last several months where people are losing their lives, people are losing their jobs and it is having a devastating effect on human life. There are frontline health Care community workers for identifying various solutions to minimize this impact. On the other hand there is a different set of people in the data science community who are looking at various different technology related solutions to assist the frontline healthcare community with their solutions. The recognition of covid-19 positive belongings at the early stage in order to preclude the blowout is most crucial. The RT-PCR is a technique to analyse the occurrence of covid-19 by taking and nasal or throat swab from the patients which perceives the capacity of antibodies which are formed by the resistant system. This is the unintended technique of analysis the presence of virus and the antibodies can display amid 7 to 28 days afterward the contagion. But the radiologists proved that the presence of covid-19 virus can be detected by using the changes that has been occurred in chest X-ray images. Due to the limited number of radiologist present across the world there is a challenge for determination of covid-19 using x-ray images. This work aims to represent a framework in order to automatically diagnose covid-19 in x-ray images using multi image augmented deep learning model. The filtered images from the CNN produces discontinuity in their information which can be resolved using multi image augmentation technique thereby accumulative the amount of images for preparation of the CNN model. This simulation has been done through to the databases which are available publicly and the proposed technique provides higher accuracy, sensitivity and specificity.
Kanthimathi, N., Saranya, N., Moses, M., & Pushpavalli, M. (2021). Automated Recognition Of COVID-19 Cases From Chest X-Ray Images Using Multi-Image Augmented Deep Learning Model. Int. J. of Aquatic Science, 12(3), 1830-1838.
MLA
N. Kanthimathi; N. Saranya; M.Leeban Moses; M. Pushpavalli. "Automated Recognition Of COVID-19 Cases From Chest X-Ray Images Using Multi-Image Augmented Deep Learning Model". Int. J. of Aquatic Science, 12, 3, 2021, 1830-1838.
HARVARD
Kanthimathi, N., Saranya, N., Moses, M., Pushpavalli, M. (2021). 'Automated Recognition Of COVID-19 Cases From Chest X-Ray Images Using Multi-Image Augmented Deep Learning Model', Int. J. of Aquatic Science, 12(3), pp. 1830-1838.
VANCOUVER
Kanthimathi, N., Saranya, N., Moses, M., Pushpavalli, M. Automated Recognition Of COVID-19 Cases From Chest X-Ray Images Using Multi-Image Augmented Deep Learning Model. Int. J. of Aquatic Science, 2021; 12(3): 1830-1838.
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