Metric-Based Meta-Learning Approach for Few-Shot Classification of Brain Tumors Using Magnetic Resonance Images

Electronics, Journal Year: 2025, Volume and Issue: 14(9), P. 1863 - 1863

Published: May 2, 2025

Brain tumor prediction from magnetic resonance images is an important problem, but it difficult due to the complexity of brain structure and variability in appearance. There have been various ML DL-based approaches, limitations current models are a lack adaptability new tasks need for extensive training on large datasets. To address these issues, novel meta-learning approach has proposed, enabling rapid adaptation with limited data. This paper presents method that integrates vision transformer metric-based model, few-shot learning enhance classification performance. The proposed begins preprocessing MRI images, followed by feature extraction using transformer. A Siamese network enhances model’s learning, quick unseen data improving robustness. Furthermore, applying strategy performance when there comparison other developed reveals consistently performs better. It also compared previously approaches same datasets evaluation metrics including accuracy, precision, specificity, recall, F1-score. results demonstrate efficacy our methodology classification, which significant implications enhancing diagnostic accuracy patient outcomes.

Language: Английский

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Advancements in Brain Tumour Analysis: A Review of Machine Learning, Deep Learning, Image Processing, and Explainable AI Techniques

Operations Research Forum, Journal Year: 2025, Volume and Issue: 6(2)

Published: May 5, 2025

Language: Английский

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Comparison based on transfer learning and fusion of deep learning models for brain cancer classification

Multimedia Tools and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: May 10, 2025

Language: Английский

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Advancements in Healthcare Medical Imaging through SHO optimized CNN

Procedia Computer Science, Journal Year: 2025, Volume and Issue: 258, P. 4128 - 4135

Published: Jan. 1, 2025

Language: Английский

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GraphMriNet: a few-shot brain tumor MRI image classification model based on Prewitt operator and graph isomorphic network

Complex & Intelligent Systems, Journal Year: 2024, Volume and Issue: 10(5), P. 6917 - 6930

Published: June 28, 2024

Abstract Brain tumors are regarded as one of the most lethal forms cancer, primarily due to their heterogeneity and low survival rates. To tackle challenge posed by brain tumor diagnostic models, which typically require extensive data for training often confined a single dataset, we propose model based on Prewitt operator graph isomorphic network. Firstly, during construction stage, edge information is extracted from MRI (magnetic resonance imaging) images using filtering algorithm. Pixel points with gray value intensity greater than 128 designated nodes, while remaining pixel treated edges graph. Secondly, inputted into GIN training, parameters optimized enhance performance. Compared existing work small sample sizes, GraphMriNet has achieved classification accuracies 100%, 99.68% BMIBTD, CE-MRI, BTC-MRI, FSB open datasets, respectively. The accuracy improved 0.8% 5.3% compared research. In few-shot scenario, can accurately diagnose various types tumors, providing crucial clinical guidance assist doctors in making correct medical decisions. Additionally, source code available at this link: https://github.com/keepgoingzhx/GraphMriNet .

Language: Английский

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