COVID-19 Classification based on Neutrosophic Set Transfer Learning Approach
Keywords:COVID-19; Chest X-ray; Neutrosophic set; ResNet-50, Classification.
The COVID-19 virus has a significant impact on individuals around the globe. The early diagnosis of this infectious disease is critical to preventing its global and local spread. In general, scientists have tested numerous ways and methods to detect people and analyze the virus. Interestingly, one of the methods used for COVID-19 diagnosis is X-rays that recognize whether the person is infected or not. Furthermore, the researchers attempted to use deep learning approaches that yielded quicker and more accurate results. This paper used the ResNet-50 module based on the Neutrosophic (NS) domain to diagnose COVID patients over a balanced database collected from a COVID-19 radiography database. The method is a future work of the N. E. M. Khalifa et al.’s method for NS set significance on deep transfer learning. True (T), False (F), and Indeterminate (I) membership sets were used to define chest X-ray images in the NS domain. Experimental results confirmed that the proposed approach achieved a 98.05% accuracy rate outperforming the accuracy value acquired from previously conducted studies within the same database.
N. E. M. Khalifa, F. Smarandache, G. Manogaran, and M. Loey, “A Study of the Neutrosophic Set Significance on Deep Transfer Learning Models: an Experimental Case on a Limited COVID-19 Chest X-ray Dataset,” Cognit. Comput., no. 0123456789, 2021, doi: 10.1007/s12559-020-09802-9.
S. Saadat, D. Rawtani, and C. M. Hussain, “Environmental perspective of COVID-19,” Sci. Total Environ., vol. 728, p. 138870, 2020, doi: 10.1016/j.scitotenv.2020.138870.
S. P. Kaur and V. Gupta, “COVID-19 Vaccine: A comprehensive status report,” Virus Res., vol. 288, no. July, p. 198114, 2020, doi: 10.1016/j.virusres.2020.198114.
P. K. Sethy, S. K. Behera, P. K. Ratha, and P. Biswas, “Detection of coronavirus disease (COVID-19) based on deep features and support vector machine,” Int. J. Math. Eng. Manag. Sci., vol. 5, no. 4, pp. 643–651, 2020, doi: 10.33889/IJMEMS.2020.5.4.052.
N. Sharma, V. Jain, and A. Mishra, “An Analysis of Convolutional Neural Networks for Image Classification,” Procedia Comput. Sci., vol. 132, no. Iccids, pp. 377–384, 2018, doi: 10.1016/j.procs.2018.05.198.
A. A. Salama, S. F. Ali, H. El Ghawalby, and A. A. Salama, “From image to neutrosophic image,” no. March 2018, 2015, [Online]. Available: http://doi.org/10.13140/RG.2.2.14733.64486.
S. Lawton, “Detection of COVID-19 from CT Lung Scans Using,” vol. 2021, 2021.
A. Narin, C. Kaya, and Z. Pamuk, “Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks,” Pattern Anal. Appl., vol. 24, no. 3, pp. 1207–1220, 2021, doi: 10.1007/s10044-021-00984-y.
T. Ilyas, D. Mahmood, G. Ahmed, and A. Akhunzada, “Symptom analysis using fuzzy logic for detection and monitoring of covid-19 patients,” Energies, vol. 14, no. 21, 2021, doi: 10.3390/en14217023.
S. V J and J. F. D, “Deep Learning Algorithm for COVID-19 Classification Using Chest X-Ray Images,” Comput. Math. Methods Med., vol. 2021, p. 9269173, 2021, doi: 10.1155/2021/9269173.
S. Hira, A. Bai, and S. Hira, “An automatic approach based on CNN architecture to detect Covid-19 disease from chest X-ray images,” Appl. Intell., vol. 51, no. 5, pp. 2864–2889, 2021, doi: 10.1007/s10489-020-02010-w.
A. K. Singh, A. Kumar, M. Mahmud, M. S. Kaiser, and A. Kishore, “COVID-19 Infection Detection from Chest X-Ray Images Using Hybrid Social Group Optimization and Support Vector Classifier,” Cognit. Comput., no. 0123456789, 2021, doi: 10.1007/s12559-021-09848-3.
F. Saiz and I. Barandiaran, “COVID-19 Detection in Chest X-ray Images using a Deep Learning Approach,” Int. J. Interact. Multimed. Artif. Intell., vol. 6, no. 2, p. 4, 2020, doi: 10.9781/ijimai.2020.04.003.
A. Helwan, M. K. S. Ma’Aitah, H. Hamdan, D. U. Ozsahin, and O. Tuncyurek, “Radiologists versus Deep Convolutional Neural Networks: A Comparative Study for Diagnosing COVID-19,” Comput. Math. Methods Med., vol. 2021, 2021, doi: 10.1155/2021/5527271.
M. Alruwaili, A. Shehab, and S. Abd El-Ghany, “COVID-19 Diagnosis Using an Enhanced Inception-ResNetV2 Deep Learning Model in CXR Images,” J. Healthc. Eng., vol. 2021, no. Dl, 2021, doi: 10.1155/2021/6658058.
V. N. M. Aradhya, M. Mahmud, D. S. Guru, B. Agarwal, and M. S. Kaiser, “One-shot Cluster-Based Approach for the Detection of COVID–19 from Chest X–ray Images,” Cognit. Comput., vol. 13, no. 4, pp. 873–881, 2021, doi: 10.1007/s12559-020-09774-w.
D. Ji, Z. Zhang, Y. Zhao, and Q. Zhao, “Research on Classification of COVID-19 Chest X-Ray Image Modal Feature Fusion Based on Deep Learning,” J. Healthc. Eng., vol. 2021, 2021, doi: 10.1155/2021/6799202.
P. Gaur, V. Malaviya, A. Gupta, G. Bhatia, R. B. Pachori, and D. Sharma, “COVID-19 disease identification from chest CT images using empirical wavelet transformation and transfer learning,” Biomed. Signal Process. Control, vol. 71, no. PA, p. 103076, 2022, doi: 10.1016/j.bspc.2021.103076.
T. S. Qaid, H. Mazaar, M. Y. H. Al-shamri, M. S. Alqahtani, A. A. Raweh, and W. Alakwaa, “Hybrid Deep-Learning and Machine-Learning Models for Predicting COVID-19,” vol. 2021, 2021.
S. A. J. Zaidi, S. Tariq, and S. B. Belhaouari, “Future prediction of covid-19 vaccine trends using a voting classifier,” Data, vol. 6, no. 11, 2021, doi: 10.3390/data6110112.
V. Bahel and S. Pillai, Detection of COVID-19 Using Chest Radiographs with Intelligent Deployment Architecture. 2020.
S. H. Wady, R. Z. Yousif, and H. R. Hasan, “A Novel Intelligent System for Brain Tumor Diagnosis Based on a Composite Neutrosophic-Slantlet Transform Domain for Statistical Texture Feature Extraction,” Biomed Res. Int., vol. 2020, 2020, doi: 10.1155/2020/8125392.
O. G. El Barbary and R. Abu Gdairi, “Neutrosophic Logic-Based Document Summarization,” J. Math., vol. 2021, 2021, doi: 10.1155/2021/9938693.
A. Rashno and S. Sadri, “Content-based image retrieval with color and texture features in neutrosophic domain,” 3rd Int. Conf. Pattern Anal. Image Anal. IPRIA 2017, no. Ipria, pp. 50–55, 2017, doi: 10.1109/PRIA.2017.7983063.
E. Rezende, G. Ruppert, T. Carvalho, F. Ramos, and P. De Geus, “Malicious software classification using transfer learning of ResNet-50 deep neural network,” Proc. - 16th IEEE Int. Conf. Mach. Learn. Appl. ICMLA 2017, vol. 2017-Decem, pp. 1011–1014, 2017, doi: 10.1109/ICMLA.2017.00-19.
V. Sangeetha and K. J. R. Prasad, “Syntheses of novel derivatives of 2-acetylfuro[2,3-a]carbazoles, benzo[1,2-b]-1,4-thiazepino[2,3-a]carbazoles and 1-acetyloxycarbazole-2- carbaldehydes,” Indian J. Chem. - Sect. B Org. Med. Chem., vol. 45, no. 8, pp. 1951–1954, 2006, doi: 10.1002/chin.200650130.
A. Afifi, N. E. Hafsa, M. A. S. Ali, A. Alhumam, and S. Alsalman, “An ensemble of global and local-attention based convolutional neural networks for COVID-19 diagnosis on chest X-ray images,” Symmetry (Basel)., vol. 13, no. 1, pp. 1–25, 2021, doi: 10.3390/sym13010113.
M. Abd Elaziz, A. Dahou, N. A. Alsaleh, A. H. Elsheikh, A. I. Saba, and M. Ahmadein, “Boosting covid-19 image classification using mobilenetv3 and aquila optimizer algorithm,” Entropy, vol. 23, no. 11, pp. 1–17, 2021, doi: 10.3390/e23111383.
F. H. Ahmad and S. H. Wady, “COVID 19 Infection Detection from Chest X Ray Images Using Feature Fusion and Machine Learning,” Sci. J. Cihan Univ. – Sulaimaniya, vol. 5, no. 2, pp. 10–30, 2021.
S. Walvekar and S. Shinde, “International Conference on Communication and Information Processing Detection of COVID-19 from CT Images Using resnet50,” 2020, [Online]. Available: https://ssrn.com/abstract=3648863.
I. D. Apostolopoulos and T. A. Mpesiana, “Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks,” Phys. Eng. Sci. Med., vol. 43, no. 2, pp. 635–640, 2020, doi: 10.1007/s13246-020-00865-4.
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