Abstract
A pandemic called COVID-19 has threatened the world with its high morbific and transmission rate. It is vital to accurately detect and determine the traces of the infection as it caused around 62L deaths. Numerous researchers have set out to propose a virus detection solution using chest computed tomography scans based on deep learning. Yet, an accurate comparison of these techniques is not available. Within this document, a convolutional neural network model is suggested that assists in enhancing accuracy to detect the virus using CT scans by incorporating distinct filters into the model. The proposed model has attained accuracy of 0.86 unfiltered, incorporating Gabor filter helped achieve an accuracy of 0.93, and an accuracy of 0.85 is attained using bilateral, non-local means, and hybrid filtering techniques.
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Dommeti, D., Nallapati, S.R.K., Srinivas, P.V.V.S., Mandhala, V.N. (2023). Repercussions of Incorporating Filters in CNN Model to Boost the Diagnostic Ability of SARS-CoV-2 Virus Using Chest Computed Tomography Scans. In: Ogudo, K.A., Saha, S.K., Bhattacharyya, D. (eds) Smart Technologies in Data Science and Communication. Lecture Notes in Networks and Systems, vol 558. Springer, Singapore. https://doi.org/10.1007/978-981-19-6880-8_22
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