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A Few-Shot Learning Approach for Covid-19 Diagnosis Using Quasi-Configured Topological Spaces

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W.M. Shaban, A.H. Rabie, A.I. Saleh, M.A. Abo-Elsoud, A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier, Knowledge-Based Syst. 205 (2020) 106270. Search in Google Scholar

J. Li, C. Huang, Y. Yang, J. Liu, X. Lin, J. Pan, How nursing students’ risk perception affected their professional commitment during the COVID-19 pandemic: the mediating effects of negative emotions and moderating effects of psychological capital, Humanit. Soc. Sci. Commun. 10 (2023) 1–9. Search in Google Scholar

M.M. Islam, F. Karray, R. Alhajj, J. Zeng, A review on deep learning techniques for the diagnosis of novel coronavirus (COVID-19), Ieee Access. 9 (2021) 30551–30572. Search in Google Scholar

Q. Zhang, Y. Wang, R.-T. Bai, B.-R. Lian, Y. Zhang, L.-M. Cao, X-linked Charcot-Marie-Tooth disease after SARS-CoV-2 vaccination mimicked stroke-like episodes: A case report, World J. Clin. Cases. 11 (2023) 464. Search in Google Scholar

T. Sharma, R. Nair, S. Gomathi, Breast cancer image classification using transfer learning and convolutional neural network, Int. J. Mod. Res. 2 (2022) 8–16. Search in Google Scholar

I. Chatterjee, Artificial intelligence and patentability: review and discussions, Int. J. Mod. Res. 1 (2021) 15–21. Search in Google Scholar

W. Dang, L. Xiang, S. Liu, B. Yang, M. Liu, Z. Yin, L. Yin, W. Zheng, A Feature Matching Method based on the Convolutional Neural Network., J. Imaging Sci. Technol. 67 (2023). Search in Google Scholar

P.K. Vaishnav, S. Sharma, P. Sharma, Analytical review analysis for screening COVID-19 disease, Int. J. Mod. Res. 1 (2021) 22–29. Search in Google Scholar

W. Wang, F. Qi, D. Wipf, C. Cai, T. Yu, Y. Li, Z. Yu, W. Wu, Sparse Bayesian Learning for Endto-End EEG Decoding, IEEE Trans. Pattern Anal. Mach. Intell. (2023). Search in Google Scholar

B. Cheng, D. Zhu, S. Zhao, J. Chen, Situation-aware IoT service coordination using the event-driven SOA paradigm, IEEE Trans. Netw. Serv. Manag. 13 (2016) 349–361. Search in Google Scholar

X. Shen, S.-C. Du, Y.-N. Sun, P.Z.H. Sun, R. Law, E.Q. Wu, Advance Scheduling for Chronic care under online or Offline revisit uncertainty, IEEE Trans. Autom. Sci. Eng. (2023). Search in Google Scholar

S. Lu, J. Yang, B. Yang, Z. Yin, M. Liu, L. Yin, W. Zheng, Analysis and Design of Surgical Instrument Localization Algorithm., C. Model. Eng. Sci. 137 (2023). Search in Google Scholar

X. Yi, X. Guan, C. Chen, Y. Zhang, Z. Zhang, M. Li, P. Liu, A. Yu, X. Long, L. Liu, Adrenal incidentaloma: machine learning-based quantitative texture analysis of unenhanced CT can effectively differentiate sPHEO from lipid-poor adrenal adenoma, J. Cancer. 9 (2018) 3577. Search in Google Scholar

Z.A.A. Alyasseri, M.A. Al-Betar, I.A. Doush, M.A. Awadallah, A.K. Abasi, S.N. Makhadmeh, O.A. Alomari, K.H. Abdulkareem, A. Adam, R. Damasevicius, Review on COVID-19 diagnosis models based on machine learning and deep learning approaches, Expert Syst. 39 (2022) e12759. Search in Google Scholar

S. Lawrence, C.L. Giles, Overfitting and neural networks: conjugate gradient and backpropagation, in: Proc. IEEE-INNS-ENNS Int. Jt. Conf. Neural Networks. IJCNN 2000. Neural Comput. New Challenges Perspect. New Millenn., IEEE, 2000: pp. 114–119. Search in Google Scholar

N. Wang, J. Chen, W. Chen, Z. Shi, H. Yang, P. Liu, X. Wei, X. Dong, C. Wang, L. Mao, The effectiveness of case management for cancer patients: an umbrella review, BMC Health Serv. Res. 22 (2022) 1–20. Search in Google Scholar

S.K. Shukla, V.K. Gupta, K. Joshi, A. Gupta, M.K. Singh, Self-aware execution environment model (SAE2) for the performance improvement of multicore systems, Int. J. Mod. Res. 2 (2022) 17–27. Search in Google Scholar

T. Clarke, P. Ayres, J. Sweller, The impact of sequencing and prior knowledge on learning mathematics through spreadsheet applications, Educ. Technol. Res. Dev. (2005) 15–24. Search in Google Scholar

Y. Wang, Q. Yao, J.T. Kwok, L.M. Ni, Generalizing from a few examples: A survey on few-shot learning, ACM Comput. Surv. 53 (2020) 1–34. Search in Google Scholar

J. Vanschoren, Meta-learning, Autom. Mach. Learn. Methods, Syst. Challenges. (2019) 35–61. Search in Google Scholar

T. Hospedales, A. Antoniou, P. Micaelli, A. Storkey, Meta-learning in neural networks: A survey, IEEE Trans. Pattern Anal. Mach. Intell. 44 (2021) 5149–5169. Search in Google Scholar

J.R. Anderson, Is human cognition adaptive?, Behav. Brain Sci. 14 (1991) 471–485. Search in Google Scholar

J.L. McClelland, M.M. Botvinick, D.C. Noelle, D.C. Plaut, T.T. Rogers, M.S. Seidenberg, L.B. Smith, Letting structure emerge: connectionist and dynamical systems approaches to cognition, Trends Cogn. Sci. 14 (2010) 348–356. Search in Google Scholar

Y. Ma, G. Zhong, W. Liu, Y. Wang, P. Jiang, R. Zhang, ML-CGAN: conditional generative adversarial network with a meta-learner structure for high-quality image generation with few training data, Cognit. Comput. 13 (2021) 418–430. Search in Google Scholar

Y. Zhang, B. Lian, S. Yang, X. Huang, Y. Zhou, L. Cao, Metabotropic glutamate receptor 5-related autoimmune encephalitis with reversible splenial lesion syndrome following SARS-CoV-2 vaccination, Medicine (Baltimore). 102 (2023). Search in Google Scholar

Z. Gao, X. Pan, J. Shao, X. Jiang, Z. Su, K. Jin, J. Ye, Automatic interpretation and clinical evaluation for fundus fluorescein angiography images of diabetic retinopathy patients by deep learning, Br. J. Ophthalmol. (2022). Search in Google Scholar

Y. Liu, Y. Wu, X. Shen, L. Xie, COVID-19 multi-targeted drug repurposing using few-shot learning, Front. Bioinforma. 1 (2021) 693177. Search in Google Scholar

Y. Wang, C. Jiang, Y. Wu, T. Lv, H. Sun, Y. Liu, L. Li, X. Pan, Semantic-Powered Explainable Model-Free Few-Shot Learning Scheme of Diagnosing COVID-19 on Chest X-ray, IEEE J. Biomed. Heal. Informatics. 26 (2022) 5870–5882. Search in Google Scholar

W. Li, Diagnose COVID-19 Based on CT Images Using Transfer Learning, in: 2021 2nd Int. Conf. Artif. Intell. Comput. Eng., IEEE, 2021: pp. 740–744. Search in Google Scholar

X. Chen, L. Yao, T. Zhou, J. Dong, Y. Zhang, Momentum contrastive learning for few-shot COVID-19 diagnosis from chest CT images, Pattern Recognit. 113 (2021) 107826. Search in Google Scholar

Y. Ge, Y. Guo, Y.-C. Yang, M.A. Al-Garadi, A. Sarker, Few-shot learning for medical text: A systematic, (n.d.). Search in Google Scholar

Y. Jiang, H. Chen, H. Ko, D.K. Han, Few-shot learning for ct scan based covid-19 diagnosis, in: ICASSP 2021-2021 IEEE Int. Conf. Acoust. Speech Signal Process., IEEE, 2021: pp. 1045–1049. Search in Google Scholar

R. Abdrakhmanov, M. Altynbekov, A. Abu, A. Shomanov, D. Viderman, M.-H. Lee, Few-shot learning approach for COVID-19 detection from X-ray images, in: 2021 16th Int. Conf. Electron. Comput. Comput., IEEE, 2021: pp. 1–3. Search in Google Scholar

M. Abdel-Basset, V. Chang, H. Hawash, R.K. Chakrabortty, M. Ryan, FSS-2019-nCov: A deep learning architecture for semi-supervised few-shot segmentation of COVID-19 infection, Knowledge-Based Syst. 212 (2021) 106647. Search in Google Scholar

P. Singh, P. Mazumder, Dual class representation learning for few-shot image classification, Knowledge-Based Syst. 238 (2022) 107840. Search in Google Scholar

X. Wang, Y. Yuan, D. Guo, X. Huang, Y. Cui, M. Xia, Z. Wang, C. Bai, S. Chen, SSA-Net: Spatial self-attention network for COVID-19 pneumonia infection segmentation with semi-supervised few-shot learning, Med. Image Anal. 79 (2022) 102459. Search in Google Scholar

M. Shorfuzzaman, M.S. Hossain, MetaCOVID: A Siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patients, Pattern Recognit. 113 (2021) 107700. Search in Google Scholar

R. Abdrakhmanov, D. Viderman, K.-S. Wong, M. Lee, Few-Shot Learning based on Residual Neural Networks for X-ray Image Classification, in: 2022 IEEE Int. Conf. Syst. Man, Cybern., IEEE, 2022: pp. 1817–1821. Search in Google Scholar

X. Yang, X. He, J. Zhao, Y. Zhang, S. Zhang, P. Xie, COVID-CT-dataset: a CT scan dataset about COVID-19, (2020). Search in Google Scholar

C. Interiano, S. Muze, B. Turner, M. Gonzalez, B. Rogers, R. Jerris, E. Weinzierl, M. Elkhalifa, V. Leung-Pineda, Dataset for longitudinal evaluation of the Abbott ARCHITECT SARS-CoV-2 IgM and IgG assays in a pediatric population divided by age, Data Br. 36 (2021) 107110. Search in Google Scholar

E. Neri, V. Miele, F. Coppola, R. Grassi, Use of CT and artificial intelligence in suspected or COVID-19 positive patients: statement of the Italian Society of Medical and Interventional Radiology, Radiol. Med. 125 (2020) 505–508. Search in Google Scholar

Y. Zhuang, N. Jiang, Y. Xu, Progressive distributed and parallel similarity retrieval of large CT image sequences in mobile telemedicine networks, Wirel. Commun. Mob. Comput. 2022 (2022) 1–13. Search in Google Scholar

S. Lu, B. Yang, Y. Xiao, S. Liu, M. Liu, L. Yin, W. Zheng, Iterative reconstruction of low-dose CT based on differential sparse, Biomed. Signal Process. Control. 79 (2023) 104204. Search in Google Scholar

F. Pahde, M. Puscas, T. Klein, M. Nabi, Multi-modal prototypical networks for few-shot learning, in: Proc. IEEE/CVF Winter Conf. Appl. Comput. Vis., 2021: pp. 2644–2653. Search in Google Scholar

Y. Gong, Y. Yue, W. Ji, G. Zhou, Cross-domain few-shot learning based on pseudo-Siamese neural network, Sci. Rep. 13 (2023) 1427. Search in Google Scholar

X. Xu, Z. Wang, Z. Chi, H. Yang, W. Du, Complementary features based prototype self-updating for few-shot learning, Expert Syst. Appl. 214 (2023) 119067. Search in Google Scholar

Z. Wang, P. Ma, Z. Chi, D. Li, H. Yang, W. Du, Multi-attention mutual information distributed framework for few-shot learning, Expert Syst. Appl. 202 (2022) 117062. Search in Google Scholar

B. Oreshkin, P. Rodríguez López, A. Lacoste, Tadam: Task dependent adaptive metric for improved few-shot learning, Adv. Neural Inf. Process. Syst. 31 (2018). Search in Google Scholar

P. Tian, W. Li, Y. Gao, Consistent meta-regularization for better meta-knowledge in few-shot learning, IEEE Trans. Neural Networks Learn. Syst. 33 (2021) 7277–7288. Search in Google Scholar

J. Snell, K. Swersky, R. Zemel, Prototypical networks for few-shot learning, Adv. Neural Inf. Process. Syst. 30 (2017). Search in Google Scholar

X. Luo, H. Wu, J. Zhang, L. Gao, J. Xu, J. Song, A Closer Look at Few-shot Classification Again, ArXiv Prepr. ArXiv2301.12246. (2023). Search in Google Scholar

X. Li, M. Khishe, L. Qian, Evolving deep gated recurrent unit using improved marine predator algorithm for profit prediction based on financial accounting information system, Complex Intell. Syst. (2023) 1–17. Search in Google Scholar

L. Qian, J. Bai, Y. Huang, D.Q. Zeebaree, A. Saffari, D.A. Zebari, Breast cancer diagnosis using evolving deep convolutional neural network based on hybrid extreme learning machine technique and improved chimp optimization algorithm, Biomed. Signal Process. Control. 87 (2024) 105492 Search in Google Scholar

Y. Zhang, Y. Guo, Y. Jin, Y. Luo, Z. He, H. Lee, Unsupervised discovery of object landmarks as structural representations, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2018: pp. 2694–2703. Search in Google Scholar

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Computer Sciences, Databases and Data Mining, Artificial Intelligence