Image classification and discrimination of COVID pneumonia based on convolutional neural network

Chuan Qin; Xuesong Liu; Yuanxi Che; Enjie Yao

doi:10.1117/12.2689166

11 September 2023 Image classification and discrimination of COVID pneumonia based on convolutional neural network

Chuan Qin, Xuesong Liu, Yuanxi Che, Enjie Yao

Author Affiliations +

Proceedings Volume 12779, Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023); 127792L (2023) https://doi.org/10.1117/12.2689166
Event: Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023), 2023, Kunming, China

Abstract

To address the negative quality of RT-PCR and the time taken to obtain results, this paper reports on the classification and discrimination of COVID pneumonia images by developing a neural network adaptively. Given the limited number of published COVID-19 CT data currently available, we suggest using the proposed negative process to refine the data to obtain more CT data to reduce matching risk. Experimental results show that compared to network models such as AlexNet and GoogleNet, the proposed BUF-Net network model has the best performance with an accuracy of 93%. Visualization of systemic findings using Grad-CAM techniques may further elucidate the critical role of CT imaging in the diagnosis of COVID-19. The use of deep learning tools in clinical practice can help radiologists make better diagnoses.

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Chuan Qin, Xuesong Liu, Yuanxi Che, and Enjie Yao "Image classification and discrimination of COVID pneumonia based on convolutional neural network", Proc. SPIE 12779, Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023), 127792L (11 September 2023); https://doi.org/10.1117/12.2689166

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KEYWORDS

Computed tomography

Data modeling

Deep learning

COVID 19

Chest

Image classification

Performance modeling

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