Enhanced brain tumor detection and classification using a deep image recognition generative adversarial network (DIR-GAN): a comparative study on MRI, X-ray, and FigShare datasets

Neural Computing and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 28, 2025

Language: Английский

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Dynamic Focus on Tumor Boundaries: A Lightweight U-Net for MRI Brain Tumor Segmentation

Bioengineering, Journal Year: 2024, Volume and Issue: 11(12), P. 1302 - 1302

Published: Dec. 23, 2024

Accurate segmentation of brain tumors in MRI scans is critical for diagnosis and treatment planning. Traditional models, such as U-Net, excel capturing spatial information but often struggle with complex tumor boundaries subtle variations image contrast. These limitations can lead to inconsistencies identifying regions, impacting the accuracy clinical outcomes. To address these challenges, this paper proposes a novel modification U-Net architecture by integrating attention mechanism designed dynamically focus on relevant regions within scans. This innovation enhances model's ability delineate fine improves precision. Our model was evaluated Figshare dataset, which includes annotated images meningioma, glioma, pituitary tumors. The proposed achieved Dice similarity coefficient (DSC) 0.93, recall 0.95, an AUC 0.94, outperforming existing approaches V-Net, DeepLab V3+, nnU-Net. results demonstrate effectiveness our addressing key challenges like low-contrast boundaries, small overlapping Furthermore, lightweight design ensures its suitability real-time applications, making it robust tool automated segmentation. study underscores potential mechanisms significantly enhance medical imaging models paves way more effective diagnostic tools.

Language: Английский

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Optimized deep learning model for comprehensive medical image analysis across multiple modalities

Neurocomputing, Journal Year: 2024, Volume and Issue: 619, P. 129182 - 129182

Published: Dec. 12, 2024

Language: Английский

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Predicting Paediatric Brain Disorders from MRI Images Using Advanced Deep Learning Techniques

Neuroinformatics, Journal Year: 2025, Volume and Issue: 23(2)

Published: Jan. 16, 2025

Language: Английский

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PVTAdpNet: polyp segmentation using pyramid vision transformer with a novel adapter block

International Journal of Information Technology, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 20, 2025

Language: Английский

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A novel convolution neural network architecture with fully connected network for efficient speech emotion recognition system

International Journal of Information Technology, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

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Radiomics-driven neuro-fuzzy framework for rule generation to enhance explainability in MRI-based brain tumor segmentation

Frontiers in Neuroinformatics, Journal Year: 2025, Volume and Issue: 19

Published: April 17, 2025

Brain tumors are a leading cause of mortality worldwide, with early and accurate diagnosis being essential for effective treatment. Although Deep Learning (DL) models offer strong performance in tumor detection segmentation using MRI, their black-box nature hinders clinical adoption due to lack interpretability. We present hybrid AI framework that integrates 3D U-Net Convolutional Neural Network MRI-based radiomic feature extraction. Dimensionality reduction is performed machine learning, an Adaptive Neuro-Fuzzy Inference System (ANFIS) employed produce interpretable decision rules. Each experiment constrained small set high-impact features enhance clarity reduce complexity. The was validated on the BraTS2020 dataset, achieving average DICE Score 82.94% core 76.06% edema segmentation. Classification tasks yielded accuracies 95.43% binary (healthy vs. tumor) 92.14% multi-class edema) problems. A concise 18 fuzzy rules generated provide clinically outputs. Our approach balances high diagnostic accuracy enhanced interpretability, addressing critical barrier applying DL settings. Integrating ANFIS radiomics supports transparent decision-making, facilitating greater trust applicability real-world medical diagnostics assistance.

Language: Английский

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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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Enhanced brain tumor detection and segmentation using densely connected convolutional networks with stacking ensemble learning

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 186, P. 109703 - 109703

Published: Jan. 24, 2025

Language: Английский

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M-C&M-BL: a novel classification model for brain tumor classification: multi-CNN and multi-BiLSTM

The Journal of Supercomputing, Journal Year: 2025, Volume and Issue: 81(3)

Published: Feb. 14, 2025

Language: Английский

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