Application of 3D-CNN Model in the Diagnosis of COVID-19
DOI:
https://doi.org/10.54097/hset.v16i.2608Keywords:
COVID-19, pooling layer, convolution layer, 3D-CNN, computer tomography, medical image classification.Abstract
In recent years, COVID-19 has become the largest epidemic in the world and has had a great impact on the world. The diagnosis of COVID-19 also heavily relies on lung CT. This study presents a COVID-19 CT scan categorization computer-aided diagnostic (CAD) system. The performances of two 3D-CNNmodels with the similar structures are compared. The data used from MosMedData is the Chest CT Scans with COVID-19 Related Findings Dataset. The best segmentation technique for separating the chest tissue from the rest of the CT picture is threshold segmentation. If a volume has several slice groups, the characteristics from each group are extracted and added together to create the eigenvector for the whole CT scanning volume. The combined eigenvectors are then further categorized using a straightforward multi-layer perceptron (MLP) network. One model generated a test set with an accuracy of 78.8%, while the other model generated a test set with an accuracy of 79.8%. This study demonstrates the particular potential of 3D-CNN for COVID-19 diagnosis.
Downloads
References
Wang, C., Horby, P. W., Hayden, F. G., Gao, G. F., (2020). A novel coronavirus outbreak of global health concern, The Lancet, 395, 470-473.
Wiersinga, W. J., Rhodes, A., Cheng, A. C., Peacock, S. J., Prescott, H. C., (2020) Pathophysiology, Transmission, Diagnosis, and Treatment of Coronavirus Disease 2019 (COVID-19): A Review, JAMA, 324(8), 782–793.
Shi, H., Han, X., Jiang, N., et al. (2020). Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study, Lancet Infect Dis, 20(4), 425-434.
Ou, X., Wu, J., Zhu, H., et al. (2015). Image hashing retrieval method based on deep self-learning [J], Computer Engineer-ing& Science, 37(12), 2386-2392.
Kamnitsas, K., et al. (2017). Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation, Medical Image Analysis, 36, 61-78.
Alakwaa, W., Nassef, M., Badr, A., (2017). Lung Cancer Detection and Classification with 3D Convolutional Neural Network (3D-CNN), International Journal of Advanced Computer Science and Applications, 8, No.8.
Zou, L., Zheng, J., Miao, C., M. J., Mckeown, M. J., Wang, Z. J., (2017). 3D CNN Based Automatic Diagnosis of Attention Deficit Hyperactivity Disorder Using Functional and Structural MRI, IEEE Access, 5, 23626-23636.
Hussein, S., Cao, K., Song, Q., Bagci, U., (2017). Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task Learning, Information Processing in Medical Imaging, 10265.
Tan, W., Liu, J., (2021). A 3D CNN Network with BERT for Automatic COVID-19 Diagnosis From CT-Scan Images, Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 439-445.
Morozov, S.P., Andreychenko, A.E., (2020). MosMedData: Chest CT Scans with COVID-19 Related Findings Dataset, medRxiv, doi: https://doi.org/10.1101/2020.05.20.20100362.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
