Designing a Visual Geometry Group-based Triad-Channel Convolutional Neural Network for COVID-19 Prediction | ||
| Journal of AI and Data Mining | ||
| دوره 12، شماره 3، مهر 2024، صفحه 423-434 اصل مقاله (1.63 M) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2024.15205.2626 | ||
| نویسندگان | ||
| Seyed Alireza Bashiri Mosavi* 1؛ Omid Khalaf Beigi2؛ Arash Mahjoubifard3 | ||
| 1Department of Electrical and Computer Engineering, Buein Zahra Technical University, Buein Zahra, Qazvin, Iran. | ||
| 2Department of Electrical and Computer Engineering, Kharazmi University, Tehran, Iran. | ||
| 3Department of Computer Engineering and Information Technology, University of Qom, Qom, Iran. | ||
| چکیده | ||
| Using intelligent approaches in diagnosing the COVID-19 disease based on machine learning algorithms (MLAs), as a joint work, has attracted the attention of pattern recognition and medicine experts. Before applying MLAs to the data extracted from infectious diseases, techniques such as RAT and RT-qPCR were used by data mining engineers to diagnose the contagious disease, whose weaknesses include the lack of test kits, the placement of the specialist and the patient pointed at a place and low accuracy. This study introduces a three-stage learning framework including a feature extractor by visual geometry group 16 (VGG16) model to solve the problems caused by the lack of samples, a three-channel convolution layer, and a classifier based on a three-layer neural network. The results showed that the Covid VGG16 (CoVGG16) has an accuracy of 96.37% and 100%, precision of 96.52% and 100%, and recall of 96.30% and 100% for COVID-19 prediction on the test sets of the two datasets (one type of CT-scan-based images and one type of X-ray-oriented ones gathered from Kaggle repositories). | ||
| کلیدواژهها | ||
| COVID-19 prediction؛ Convolutional neural network؛ Transfer Learning؛ Computer vision؛ Image processing | ||
| مراجع | ||
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[1] N. Arora, A.K. Banerjee, and M.L. Narasu, “The role of artificial intelligence in tackling COVID-19,” Future Virology, vol. 15, no. 11, pp.717-724, 2020.
[2] A. Scohy, A. Anantharajah, M. Bodéus, B. Kabamba-Mukadi, A. Verroken, and H. Rodriguez-Villalobos, “Low performance of rapid antigen detection test as frontline testing for COVID-19 diagnosis,” Journal of Clinical Virology, vol. 129, p.104455, 2020.
[3] T. E. Miller, W. F. G. Beltran, A. Z. Bard, T. Gogakos, M. N. Anahtar, M. G. Astudillo, D. Yang, J. Thierauf, A. S. Fisch, G. K. Mahowald, and M. J. Fitzpatrick, “Clinical sensitivity and interpretation of PCR and serological COVID‐19 diagnostics for patients presenting to the hospital,” The FASEB Journal, vol. 34, no. 10, p.13877, 2020.
[4] S. Salehi, A. Abedi, S. Balakrishnan, and A. Gholamrezanezhad, “Coronavirus disease 2019 (COVID-19): a systematic review of imaging findings in 919 patients,” American Journal of Roentgenology, vol. 215, no. 1, pp.87-93, 2020.
[5] M. M. Keikha, and Y. Kord Tamandani, “Breast Cancer Detection Using Deep Multilayer Neural Networks,” Journal of Epigenetics, vol. 3, no. 1, pp.27-34, 2022.
[6] E. Wu, K. Wu, D. Cox, and W. Lotter, “Conditional infilling GANs for data augmentation in mammogram classification,” in Image Analysis for Moving Organ, Breast, and Thoracic Images: Third International Workshop, RAMBO 2018, Fourth International Workshop, BIA 2018, and First International Workshop, TIA 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, Proceedings 3. Springer International Publishing, 2018, pp. 98-106.
[7] Y. Yang, P. A. Fasching, M. Wallwiener, T. N. Fehm, S. Y. Brucker, and V. Tresp, “Predictive clinical decision support system with RNN encoding and tensor decoding,” arXiv preprint arXiv:1612.00611, 2016.
[8] V. Despotovic, M. Ismael, M. Cornil, R. Mc Call, and G. Fagherazzi, “Detection of COVID-19 from voice, cough and breathing patterns: Dataset and preliminary results,” Computers in Biology and Medicine, vol. 138, p.104944, 2021.
[9] A. M. Ismael, and A. Şengür, “Deep learning approaches for COVID-19 detection based on chest X-ray images,” Expert Systems with Applications, vol. 164, p.114054, 2021.
[10] A. Jaiswal, N. Gianchandani, D. Singh, V. Kumar, and M. Kaur, “Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning,” Journal of Biomolecular Structure and Dynamics, vol. 39, no. 15, pp.5682-5689, 2021.
[11] Y. Pathak, P. K. Shukla, and K. V. Arya, “Deep bidirectional classification model for COVID-19 disease infected patients,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 18, vol. 4, pp.1234-1241, 2020.
[12] J. Schmidhuber, and S. Hochreiter, “Long short-term memory,” Neural Comput, vol. 9, vol. 8, pp.1735-1780, 1997.
[13] C. M. Bishop, “Mixture density networks”, 1994.
[14] K. Simonyan, and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014.
[15] J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in IEEE conference on computer vision and pattern recognition, 2009, pp.248-255.
[16] E. Soares, P. Angelov, S. Biaso, M. H. Froes, and D. K. Abe, “SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification,” MedRxiv, pp.2020-04, 2020.
[17] P. Angelov, and E. Soares, "Towards explainable deep neural networks (xDNN)," Neural Networks, vol. 130, pp.185-194, 2020.
[18] J. Micah Bennett, “SMART-CT-SCAN_BASED COVID19_VIRUS_DETECTOR,” 2020. [Online]. Available:https://github.com/JordanMicahBennett/SMART-CT-SCAN_BASED COVID19_VIRUS_DETECTOR/
[19] J. P. Cohen, P. Morrison, L. Dao, K. Roth, T. Q. Duong, and M. Ghassemi, “Covid-19 image data collection: Prospective predictions are the future,” arXiv preprint arXiv:2006.11988, 2020.
[20] D. P. Kingma, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014.
[21] S. H. Khan, A. Sohail, M. M. Zafar, and A. Khan, “Coronavirus disease analysis using chest X-ray images and a novel deep convolutional neural network,” Photodiagnosis and Photodynamic Therapy, vol. 35, p.102473, 2021.
[22] S. A. Bashiri Mosavi, M. Javaherian, and O. Khalaf Beigi, “Selecting Optimal Moments of Chest Images by Partialized-Dual-Hybrid Feature Selection Scheme for Morphological-based COVID-19 Diagnosis,” Journal of AI and Data Mining, 2024. | ||
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