Volume 20 , Issue 1 , PP: 174-183, 2023 | Cite this article as | XML | Html | PDF | Review Article
M. A. El-Shorbagy 1 * , Hossam A. Nabwey 2 , Mustafa Inc 3 , Mostafa M. A. Khater 4
Doi: https://doi.org/10.54216/IJNS.200114
The fast spread of COVID-19 has been a problem for several nations since February 2020. Computer-aided diagnostic technologies that are both effective and affordable are urgently needed to help ease the burden on healthcare systems. Researchers are delving further into the feasibility of using image analysis to detect COVID-19 in X-ray and CT-scan pictures of patients. In the past ten years, deep learning has surpassed every other method for classifying images. However, deep learning-based approaches' effectiveness is very sensitive to the design of the underlying deep neural network. In recent years, metaheuristics and neutrosophic sets have become more popular as a means of fine-tuning the structure of deep networks. Because of their adaptability, simplicity, and task dependence, metaheuristics have been extensively employed to tackle many difficult non-linear optimization problems. To correctly identify COVID-19 patients from their chest X-rays, the authors of this research made a review of a neurotrophic model and metaheuristics methods.
Metaheuristics , Neutrosophic Sets , COVID-19 , X-ray ,
[1] Nivetha Martin , Florentin Smarandache , broumi said, COVID-19 Decision-Making Model using
Extended Plithogenic Hypersoft Sets with Dual Dominant Attributes, International Journal of
Neutrosophic Science, Vol. 13 , No.2 , (2021) : 75-86
[2] M. Kaur, V. Kumar, V. Yadav, D. Singh, N. Kumar, and N. N. Das, “Metaheuristic-based deep COVID-19 screening model from chest X-ray images,” Journal of Healthcare Engineering, vol. 2021, 2021.
[3] N. A. Samee et al., “Metaheuristic optimization through deep learning classification of COVID -19 in chest
X-ray images,” Computers, Materials and Continua, pp. 4193–4210, 2022.
[4] A. J. da S. Rodrigues and G. L. Lima, “A metaheuristic to support the distribution of COVID-19 vaccines,”
Production, vol. 31, 2021.
[5] S. Chakraborty and K. Mali, “SuFMoFPA: A superpixel and meta-heuristic based fuzzy image
segmentation approach to explicate COVID-19 radiological images,” Expert Systems with Applications,
vol. 167, p. 114142, 2021.
[6] M. Canayaz, “MH-COVIDNet: Diagnosis of COVID-19 using deep neural networks and meta-heuristicbased feature selection on X-ray images,” Biomedical Signal Processing and Control, vol. 64, p. 102257,
2021.
[7] Marcelo Viteri Villa , Lester Wong Vázquez , Roberto Zúñiga Viteri, Neutrsophic Health Analysis in
Times of COVID-19, International Journal of Neutrosophic Science, Vol. 18 , No. 3 , (2022) : 218 -226
[8] J. De Anda-Suárez, V. Calzada-Ledesma, D. A. Gutiérrez-Hernández, R. Santiago-Montero, L. F.
Villanueva-Jiménez, and S. Rodríguez-Miranda, “A novel metaheuristic framework based on the
generalized boltzmann distribution for COVID-19 spread characterization,” IEEE Access, vol. 10, pp.
7326–7340, 2022.
[9] L. Fenga and C. Del Castello, “COVID-19: Metaheuristic Optimization-Based Forecast Method on TimeDependent Bootstrapped Data,” Journal of Probability and Statistics, vol. 2021, 2020.
[10] J. Dey, A. Sarkar, B. Chowdhury, and S. Karforma, “Episode of COVID -19 telepsychiatry session key
origination upon swarm-based metaheuristic and neural perceptron blend,” SN Computer Science, vol. 2,
no. 6, pp. 1–16, 2021.
[11] V. Ganesan, P. Rajarajeswari, V. Govindaraj, K. B. Prakash, and J. Naren, “Post-COVID-19 Emerging
Challenges and Predictions on People, Process, and Product by Metaheuristic Deep Learning Algorithm,”
in Machine Intelligence and Soft Computing, Springer, 2021, pp. 275–287.
[12] A. Dey et al., “MRFGRO: a hybrid meta-heuristic feature selection method for screening COVID-19 using
deep features,” Scientific reports, vol. 11, no. 1, pp. 1–15, 2021.
[13] M. Zivkovic, A. Petrovic, N. Bacanin, S. Milosevic, V. Veljic, and A. Vesic, “The covid-19 images
classification by mobilenetv3 and enhanced sine cosine metaheuristics,” in Mobile Computing and
Sustainable Informatics, Springer, 2022, pp. 937–950.
[14] Z. Fei, Y. Ryeznik, A. Sverdlov, C. W. Tan, and W. K. Wong, “An overview of healthcare data analytics
with applications to the COVID-19 pandemic,” IEEE Transactions on Big Data, 2021.
[15] C. Li, P. Han, M. Zhou, and M. Gu, “Design of multimodal hub -and-spoke transportation network for
emergency relief under COVID-19 pandemic: A meta-heuristic approach,” Applied Soft Computing, p.
109925, 2022.
[16] D. Wolfinger, M. Gansterer, K. F. Doerner, and N. Popper, “A large neighbourhood search metaheuristic
for the contagious disease testing problem,” European journal of operational research, vol. 304, no. 1, pp.
169–182, 2023.
[17] J. Dey, A. Sarkar, S. Karforma, and B. Chowdhury, “Metaheuristic secured transmission in Telecare
Medical Information System (TMIS) in the face of post-COVID-19,” Journal of Ambient Intelligence and
Humanized Computing, pp. 1–22, 2021.
[18] R. A. K. Sherwani, H. Shakeel, M. Saleem, W. B. Awan, M. Aslam, and M. Farooq, “A new neutrosophic
sign test: An application to COVID-19 data,” PloS one, vol. 16, no. 8, p. e0255671, 2021.
[19] 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,” Cognitive Computation, pp. 1–10, 2021.
[20] Priyanka Majumder , Florentin Smarandache, Analyzing the Sustainability of Industry Affected in
COVID-19 Pandemic Scenario Using Cosine Similarity Measure under SVPNS and PNN Model,
International Journal of Neutrosophic Science, Vol. 19 , No. 3 , (2022) : 16 -28
[21] K. Karuppiah, B. Sankaranarayanan, S. M. Ali, and S. K. Paul, “Key challenges to sustainable
humanitarian supply chains: lessons from the covid-19 pandemic,” Sustainability, vol. 13, no. 11, p. 5850,
2021.
[22] R. Das, A. Mukherjee, and B. C. Tripathy, “Application of neutrosophic similarity measures in Covid -19,”
Annals of Data Science, vol. 9, no. 1, pp. 55–70, 2022.
[23] E. Çakır, M. A. Taş, and Z. Ulukan, “Neutrosophic Fuzzy Weighted Saving Heuristic for COVID-19
Vaccination,” in 2021 Systems and Information Engineering Design Symposium (SIEDS), 2021, pp. 1–4.
[24] I. M. Hezam, M. K. Nayeem, A. Foul, and A. F. Alrasheedi, “COVID-19 Vaccine: A neutrosophic MCDM
approach for determining the priority groups,” Results in physics, vol. 20, p. 103654, 2021.
[25] W. M. Shaban, A. H. Rabie, A. I. Saleh, and M. A. Abo-Elsoud, “A new COVID-19 Patients Detection
Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier,” Knowledge-Based
Systems, vol. 205, p. 106270, 2020.
[26] H. M. Afify, A. Darwish, K. K. Mohammed, and A. E. Hassanien, “An Automated CAD System of CT
Chest Images for COVID-19 Based on Genetic Algorithm and K-Nearest Neighbor Classifier.,” Ingénierie
des Systèmes d Inf., vol. 25, no. 5, pp. 589–594, 2020.
[27] T. Akram et al., “A novel framework for rapid diagnosis of COVID -19 on computed tomography scans,”
Pattern analysis and applications, vol. 24, no. 3, pp. 951–964, 2021.
[28] M. A. Albadr, S. Tiun, M. Ayob, and F. Al-Dhief, “Genetic algorithm based on natural selection theory
for optimization problems,” Symmetry, vol. 12, no. 11, p. 1758, 2020.
[29] M. Abdel-Basset, L. Abdle-Fatah, and A. K. Sangaiah, “An improved Lévy based whale optimization
algorithm for bandwidth-efficient virtual machine placement in cloud computing environment,” Cluster
Computing, vol. 22, no. 4, pp. 8319–8334, 2019.
[30] Z. Yan, S. Wang, B. Liu, and X. Li, “Application of whale optimization algorithm in optimal allocation of
water resources,” in E3S Web of Conferences, 2018, vol. 53, p. 4019.
[31] E.-S. M. El-Kenawy, A. Ibrahim, S. Mirjalili, M. M. Eid, and S. E. Hussein, “Novel feature selection and
voting classifier algorithms for COVID-19 classification in CT images,” IEEE access, vol. 8, pp. 179317–
179335, 2020.
[32] T. Goel, R. Murugan, S. Mirjalili, and D. K. Chakrabartty, “Automatic screening of covid-19 using an
optimized generative adversarial network,” Cognitive computation, pp. 1–16, 2021.
[33] M. Abdel-Basset, V. Chang, and R. Mohamed, “HSMA_WOA: A hybrid novel Slime mould algorithm
with whale optimization algorithm for tackling the image segmentation problem of chest X-ray images,”
Applied soft computing, vol. 95, p. 106642, 2020.
[34] X. Li, Z. Zhang, and C. Huang, “An EPC forecasting method for stock index based on integrating empirical
mode decomposition, SVM and cuckoo search algorithm,” Journal of Systems Science and Information,
vol. 2, no. 6, pp. 481–504, 2014.
[35] X.-S. Yang and M. Karamanoglu, “Nature-inspired computation and swarm intelligence: a state-of-the-art
overview,” Nature-Inspired Computation and Swarm Intelligence, pp. 3–18, 2020.
[36] D. Yousri, M. Abd Elaziz, L. Abualigah, D. Oliva, M. A. A. Al-Qaness, and A. A. Ewees, “COVID-19 Xray images classification based on enhanced fractional-order cuckoo search optimizer using heavy-tailed
distributions,” Applied Soft Computing, vol. 101, p. 107052, 2021.
[37] S. C. Satapathy, D. J. Hemanth, S. Kadry, G. Manogaran, N. M. S. Hannon, and V. Rajinikanth,
“Segmentation and evaluation of COVID-19 lesion from CT scan slices-a study with Kapur/Otsu function
and Cuckoo Search Algorithm,” 2020.
[38] M. Riaz, M. Bashir, and I. Younas, “Metaheuristics based COVID-19 detection using medical images: A
review,” Computers in Biology and Medicine, p. 105344, 2022.
[39] S. H. Basha, A. M. Anter, A. E. Hassanien, and A. Abdalla, “Hybrid intelligent model for classifying chest
X-ray images of COVID-19 patients using genetic algorithm and neutrosophic logic,” Soft Computing, pp.
1–16, 2021
