A few-shot learning approach for COVID-19 diagnosis using quasi-configured topological spaces

• Autorzy: Liu Hui , Wang Chunjie , Jiang Xin , Khishe Mohammad

Identyfikatory

DOI

10.2478/jaiscr-2024-0005

• Języki publikacji: EN

Abstrakty

EN

Accurate and efficient COVID-19 diagnosis is crucial in clinical settings. However, the limited availability of labeled data poses a challenge for traditional machine learning algorithms. To address this issue, we propose Turning Point (TP), a few-shot learning (FSL) approach that leverages high-level turning point mappings to build sophisticated representations across previously labeled data. Unlike existing FSL models, TP learns using quasi-configured topological spaces and efficiently combines the outputs of diverse TP learners. We evaluated TPFSL using three COVID-19 datasets and compared it with seven different benchmarks. Results show that TPFSL outperformed the top-performing benchmark models in both one-shot and five-shot tasks, with an average improvement of 4.50% and 4.43%, respectively. Additionally, TPFSL significantly outperformed the ProtoNet benchmark by 12.966% and 11.033% in one-shot and five-shot classification problems across all datasets. Ablation experiments were also conducted to analyze the impact of variables such as TP density, network topology, distance measure, and TP placement. Overall, TPFSL has the potential to improve the accuracy and speed of diagnoses for COVID-19 in clinical settings and can be a valuable tool for medical professionals.

Słowa kluczowe

EN

COVID-19; medical diagnosis; Quasi-Configured Topological Spaces; few-shot learning

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2024
• Tom: Vol. 14, No. 1
• Strony: 77--95
• Opis fizyczny: Bibliogr. 54 poz., rys.

Twórcy

autor

Liu Hui

• School of Mathematics and Statistics, Changchun University of Technology, Changchun 130012, Jilin, China
• School of Economics and Management, Changchun University of Technology, Changchun 130012, Jilin, China

autor

Wang Chunjie

• School of Mathematics and Statistics, Changchun University of Technology, Changchun 130012, Jilin, China

autor

Jiang Xin

• Department of Biochemistry, College of Basic Medical Sciences, Jilin University, Changchun 130021, Jilin, China

autor

Khishe Mohammad

• Department of Electrical Engineering, Imam Khomeini Marine Science University, Nowshahr, Iran

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