پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شماره‌ها | 610 |
| تعداد مقالات | 9,026 |
| تعداد مشاهده مقاله | 67,082,720 |
| تعداد دریافت فایل اصل مقاله | 7,656,152 |
Deep convolutional neural network classified the PNEUMONIA and Coronavirus diseases (COVID-19) by softmax nonlinearity function | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 186، دوره 13، شماره 1، خرداد 2022، صفحه 2245-2251 اصل مقاله (562.12 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.5923 | ||
| نویسندگان | ||
| Mali H. Hakem Alameady؛ Maryim Omran Mosa؛ Amir Ali Aljarrah* ؛ Huda Saleem Razzaq | ||
| Department of Computer Science, Faculty of Computer Science and Maths, University of Kufa, Najaf, Iraq | ||
| تاریخ دریافت: 11 شهریور 1400، تاریخ بازنگری: 06 آبان 1400، تاریخ پذیرش: 23 آبان 1400 | ||
| چکیده | ||
| A deep learning powerful models of machine learning indicated better performance as precision and speed for images classification. The purpose of this paper is the detection of patients suspected of pneumonia and a novel coronavirus. Convolutional Neural Network (CNN) is utilized for features extract and it classifies, where CNN classify features into three classes are COVID-19, NORMAL, and PNEUMONIA. In CNN updating weights by CNN backpropagation and SGDM optimization algorithms in the training stage. The performance of CNN on the dataset is a combination between Chest X-Ray dataset (1583-NORMAL images and 4272-PNEUMONIA images) and COVID-19 dataset (126-images) for automatically anticipate whether a patient has COVID-19 or PNEUMONIA, where accuracy 94.31\% and F1-Score 88.48\% in case 60\% training, 20\% testing, and 20\% validation. | ||
| کلیدواژه‌ها | ||
| Deep learning؛ convolutional neural network؛ and Coronavirus Disease (COVID-19) | ||
| مراجع | ||
|
[1] M. Akagi, Y. Nakamura, T. Higaki, K. Narita, Y. Honda, J. Zhou, Z. Yu, N. Akino and K. Awai, Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT, European Radiology 29(11) (2019) 6163–6171. [2] V. Aladinskiy, A. Zhebrak, B. Zagribelnyy and V. Terentiev, Potential COVID-2019 3C-like protease inhibitors designed using generative deep learning approaches, Insilico Medicine Hong Kong Ltd A 307 (2020). [3] A.A. Aljarrah and A. H. Ali, Human activity recognition using PCA and BiLSTM recurrent neural networks, 2nd Int. Conf. Engin. Technol. Appl. IEEE (2019). [4] Y. Bai, L. Yao and T. Wei, Presumed asymptomatic carrier transmission of COVID-19, Jama 323(14) (2020) 1406–1407. [5] D.J. Carretero, D. B. Pelaez, G.R. Washko and F.N. Rahaghi, Pulmonary artery–vein classification in CT images using deep learning, IEEE Trans. Medical Imag. 37(11) (2018) 2428–2440. [6] J. Cohen and D. Normile, New SARS-like virus in China triggers alarm, Sci. 367(6475) (2020) 234–235. [7] V.M. Corman, O. Landt, M. Kaiser, R. Molenkamp, A. Meijer, D.K. Chu, T. Bleicker and S. Brunink, Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR, Euro Surveill. 25(3) (2020) 2000045. [8] A.F. Gad, Practical Computer Vision Applications Using Deep Learning with CNNs, Apress, 2018. [9] M. Gharbi, J. Chen, J.T. Barron, S.W. Hasinoff and F. Durand, Deep bilateral learning for real-time image enhancement, ACM Trans. Graphics 36(4) (2017) 1–12. [10] M.H. Hesamian, W. Jia, X. He and P. Kennedy, Deep learning techniques for medical image segmentation: achievements and challenges, J. Digital Imag. 32(4) (2019) 582–596. [11] P. Kim, MATLAB Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence, Apress, 2017. [12] D.P. Kingma and J. Ba, Adam: A method for Stochastic Optimization, arXiv preprint arXiv:1412.6980, (2014)1–15. [13] P. Liu, L. Shi, W. Zhang, J. He, C. Liu, C. Zhao, S.K. Kong, J.F. Chuen Loo, D. Gu and L. Hu, Prevalence and genetic diversity analysis of human coronaviruses among cross-border children, Virology J. 14(1)(2017) 1–8. [14] X. Liu, S. Guo, B. Yang, S. Ma, H. Zhang, J. Li, C. Sun, L. Jin and X. Li, Automatic organ segmentation for CT scans based on super-pixel and convolutional neural networks, J. Digital Imag. 31(5) (2018) 748–760. [15] B. Moons, D. Bankman and M. Verhelst, Embedded Deep Learning, Springer, 2018. [16] J. Nagi, F. Ducatelle, G.A.D. Caro, D. Cire¸san, U. Meier, A. Giusti, F. Nagi and J. Schmidhube, Max-pooling convolutional neural networks for vision-based hand gesture recognition, IEEE Int Conf Signal Image Proces Appl (2011) pp. 342–347. [17] Y. Qu, E. Kang and H. Cong, Positive result of Sars-Cov-2 in sputum from a cured patient with COVID-19, Travel Medic. Infect. Disease 34 (2020) 101619. [18] M. Sokolova and G. Lapalme, A systematic analysis of performance measures for classification tasks, Inf. Process. Manag. 45(4) (2009) 427–437. [19] X. Xu, X. Jiang, C. Ma, P. Du and X. Li, A deep learning system to screen novel coronavirus disease 2019 pneumonia, Engin. 6(10) (2020) 1122–1129. [20] X. Yang, Y. Yu, J. Xu, H. Shu, J. Xia and H. Liu, Clinical course and outcomes of critically ill patients with SARSCoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study, Lancet Respiratory Medicine 8(5) (2020) 475–481. [21] P. Yu, J. Zhu, Z. Zhang and Y. Han, A familial cluster of infection associated with the 2019 novel coronavirus indicating possible person-to-person transmission during the incubation period, J. Infect. Diseases 221(11) (2020) 1757–1761. [22] W. Zhu, Y. Huang, L. Zeng, X. Chen, Y. Liu, Z. Qian, W. Fan and X. Xie, AnatomyNet: Deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy, Medical Phys. 46(2) (2019) 576–589. | ||
|
آمار تعداد مشاهده مقاله: 15,950 تعداد دریافت فایل اصل مقاله: 481 |
||