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12 April 2021 COVID-19 detection in CT images using custom weighted filter-based CNN
Foram Sanghavi, Karen Panetta, Sos Agaian
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Proceedings Volume 11734, Multimodal Image Exploitation and Learning 2021; 117340L (2021) https://doi.org/10.1117/12.2587960
Event: SPIE Defense + Commercial Sensing, 2021, Online Only
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Abstract
The Coronavirus (Covid-19) pandemic has been affecting the health of people around the globe. With the number of confirmed cases and deaths still rising daily, it is now crucial to quickly detect the positive cases and provide them with the necessary treatment. Presently, several research investigations are being conducted to help control the spread of this epidemic. One research topic is to create faster and more accurate detection. Recent studies have demonstrated that chest CT images encompass the distinctive COVID-19 features, which can be utilized for achieving an efficient COVID-19 diagnosis. However, manually reading these images on a large scale can be laborious and is intractable. Thus, using an artificial intelligence-based system that can help capture the precise information and give an accurate diagnosis would be beneficial. In this paper, a customized weighted filter-based CNN (CCNN) is proposed. Computer simulations show that the proposed CCNN system (1) increases the effectiveness of detect COVID-19 CT scans from the non- COVID-19 CT scans and (2) has faster training time compared to the traditional deep learning models.
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Foram Sanghavi, Karen Panetta, and Sos Agaian "COVID-19 detection in CT images using custom weighted filter-based CNN", Proc. SPIE 11734, Multimodal Image Exploitation and Learning 2021, 117340L (12 April 2021); https://doi.org/10.1117/12.2587960
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KEYWORDS
Computed tomography
Chest
Computer simulations
Intelligence systems
Neural networks
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