IJSSER

Title: The Development of An Accurate MobileNetV2 Computer Vision Algorithm for the Diagnosis of SARS-COV-2
Authors: Saketh Nandam and Rithvik Pathuri
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Saketh Nandam and Rithvik Pathuri Innovate AI
MLA 8 Nandam, Saketh, and Rithvik Pathuri. "The Development of An Accurate MobileNetV2 Computer Vision Algorithm for the Diagnosis of SARS-COV-2." Int. j. of Social Science and Economic Research, vol. 9, no. 11, Nov. 2024, pp. 5615-5620, doi.org/10.46609/IJSSER.2024.v09i11.041. Accessed Nov. 2024. APA 6 Nandam, S., & Pathuri, R. (2024, November). The Development of An Accurate MobileNetV2 Computer Vision Algorithm for the Diagnosis of SARS-COV-2. Int. j. of Social Science and Economic Research, 9(11), 5615-5620. Retrieved from https://doi.org/10.46609/IJSSER.2024.v09i11.041 Chicago Nandam, Saketh, and Rithvik Pathuri. "The Development of An Accurate MobileNetV2 Computer Vision Algorithm for the Diagnosis of SARS-COV-2." Int. j. of Social Science and Economic Research 9, no. 11 (November 2024), 5615-5620. Accessed November, 2024. https://doi.org/10.46609/IJSSER.2024.v09i11.041.
References [1] . Battersby, B., Ture, E., & Lam, R. (2020, May 20). Tracking the $9 Trillion Global Fiscal Support to Fight COVID-19. IMF. https://www.imf.org/en/Blogs/Articles/2020/05/20/ tracking-the-9-trillion-global-fiscal-support-to-fight-covid-19 [2] . CDC. (2020a, February 11). Labs. Centers for Disease Control and Prevention. https:// archive.cdc.gov/www_cdc_gov/coronavirus/2019-ncov/lab/resources/antigen-tests- guidelines.html [3] . CDC. (2020b, February 11). Labs. Centers for Disease Control and Prevention. https:// archive.cdc.gov/www_cdc_gov/coronavirus/2019-ncov/lab/naats.html [4] . CDC. (2024, July 15). About COVID-19. COVID-19. https://www.cdc.gov/covid/about/ index.html [5] . Coronavirus death toll. Worldometer. (n.d.). https://www.worldometers.info/coronavirus/ coronavirus-death-toll/ [6] . COVID-19 and the Lungs \| NHLBI, NIH. (2022, December 28). Www.nhlbi.nih.gov. https:// www.nhlbi.nih.gov/covid/lungs [7] . Hamming, I., Timens, W., Bulthuis, M., Lely, A., Navis, G., & van Goor, H. (2004). Tissue distribution of ACE2 protein, the functional receptor for SARS coronavirus. A first step in understanding SARS pathogenesis. The Journal of Pathology, 203(2), 631– 637. https://doi.org/10.1002/path.1570 [8] . Mayo Clinic . (2020, August 18). COVID-19 (coronavirus): Long-term Effects. Mayo Clinic. https://www.mayoclinic.org/diseases-conditions/coronavirus/in-depth/coronavirus- long-term-effects/art-20490351 [9] . National Foundation for Infectious Diseases. (2023, March). Coronaviruses. Https:// Www.nfid.org/. https://www.nfid.org/infectious-disease/coronaviruses/ [10] . Sawicka, B., Aslan, I., Della Corte, V., Periasamy, A., Krishnamurthy, S. K., Mohammed, A., Tolba Said, M. M., Saravanan, P., Del Gaudio, G., Adom, D., Sawicki, B., Nevola, G., Hanchate, D. B., & Umachandran, K. (2022). The Coronavirus Global Pandemic and Its Impacts on Society. Coronavirus Drug Discovery, 1(1), 267–311. https://doi.org/ 10.1016/b978-0-323-85156-5.00037-7 [11] . Sharma, A., Balda, S., Apreja, M., Kataria, K., Capalash, N., & Sharma, P. (2021).COVID-19 Diagnosis: Current and Future Techniques. International Journal of Biological Macromolecules, 193. https://doi.org/10.1016/j.ijbiomac.2021.11.016 [12] . The Long-Term Effects of SARS-CoV-2 on Organs and Energy \| NIH COVID-19 Research. (2023, October 18). Covid19.Nih.gov. https://covid19.nih.gov/news-and-stories/long- term-effects-sars-cov-2-organs-and- energy#:~:text=What%20you%20need%20to%20know [13] . World Health Organization. (2024). COVID-19 deaths \| WHO COVID-19 dashboard. Datadot. https://data.who.int/dashboards/covid19/deaths?n=o [14] . Zalzala, H. H. (2020). Diagnosis of COVID-19: Facts and challenges. New Microbes and New Infections, 38, 100761. https://doi.org/10.1016/j.nmni.2020.100761
ABSTRACT: As of August 2024, the World Health Organization reported 238,416 cases of the Coronavirus, also commonly referred to as COVID-19 (WHO, 24). There has been a reported total accumulation of 776,007,137 cases since the pandemic began in March of 2020. This virus spread all around the world killing over 7 million people while deeply harming those who tested positive. ( Furthermore, COVID-19 caused significant economic downfall due to unemployment rates jumping to 5% and the major increases in global inflation. Proper diagnosis of COVID-19 is crucial towards helping society get their required medical assistance. Current traditional methods such as RT-PCR and antigen tests remain widely used, but come with their own limitations. For example, the supply of PCR kits is not sufficient to satisfy the need for rapid testing. Antigen tests have their own shortcomings, and require time to develop results. In order to fix the issue of delays and supply kit shortages, we implemented a computer vision model utilizing MobileNetV2 that inputs a patient’s chest x-ray scan and outputs the covid result. While the model use depends on having an X-ray on hand, it enables rapid diagnosis for the high risk population, which may already have the necessary prerequisites. A model proved to be the most effective approach, as it required far fewer resources without sacrificing diagnosis quality. Our model successfully utilized dependencies and convolutional neural networks to output the result of a COVID chest x-ray scan after intense training and testing. Our product eliminated the need for ineffective methods of diagnosis and proved to be far more accurate.

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