Abstract
COVID-19 emerged as a pandemic after originating in Wuhan, China, in the year 2019. As COVID-19 has affected millions of people around the globe, therefore creating a need of finding diagnostic methods for detecting COVID-19. The most used diagnostic methods are RTPCR, RAT, CT scan, and X-ray imaging. The problem with RTPCR is that even though it is the most accurate test for detecting COVID-19, it is exceptionally time-consuming; in such a case, radio-imaging comes into the picture, X-rays and CT scan images can help in faster identification of the disease, hence saving lives. This paper aims to use chest CT scan images to detect COVID-19 using deep learning techniques like convolutional neural network and VGG16. We also propose a state-of-the-art convolutional neural network called Cov-CONNET, which proves to be more efficient in classifying COVID-19 images, portrayed by the validation results. We attain a testing accuracy of 99.3689% for Cov-CONNET and a testing accuracy of 99.4229% for VGG16.
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Singh, S., Krishnan, D. (2023). Cov-CONNET: A Deep CNN Model for COVID-19 Detection. In: Sisodia, D.S., Garg, L., Pachori, R.B., Tanveer, M. (eds) Machine Intelligence Techniques for Data Analysis and Signal Processing. Lecture Notes in Electrical Engineering, vol 997. Springer, Singapore. https://doi.org/10.1007/978-981-99-0085-5_52
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DOI: https://doi.org/10.1007/978-981-99-0085-5_52
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