RanMerFormer: Randomized vision transformer with token merging for brain tumor classification

Neurocomputing, Journal Year: 2024, Volume and Issue: 573, P. 127216 - 127216

Published: Jan. 5, 2024

Brains are the control center of nervous system in human bodies, and brain tumor is one most deadly diseases. Currently, magnetic resonance imaging (MRI) effective way to tumors early detection clinical diagnoses due its superior quality for soft tissues. Manual analysis MRI error-prone which depends on empirical experience fatigue state radiologists a large extent. Computer-aided diagnosis (CAD) systems becoming more impactful because they can provide accurate prediction results based medical images with advanced techniques from computer vision. Therefore, novel CAD method classification named RanMerFormer presented this paper. A pre-trained vision transformer used as backbone model. Then, merging mechanism proposed remove redundant tokens transformer, improves computing efficiency substantially. Finally, randomized vector functional-link serves head RanMerFormer, be trained swiftly. All simulation obtained two public benchmark datasets, reveal that achieve state-of-the-art performance classification. The applied real-world scenarios assist diagnosis.

Language: Английский

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SAlexNet: Superimposed AlexNet using Residual Attention Mechanism for Accurate and Efficient Automatic Primary Brain Tumor Detection and Classification

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104025 - 104025

Published: Jan. 1, 2025

Language: Английский

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A Lightweight multi-path convolutional neural network architecture using optimal features selection for multiclass classification of brain tumor using magnetic resonance images

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104327 - 104327

Published: Feb. 1, 2025

Language: Английский

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Detection of MRI brain tumor using residual skip block based modified MobileNet model

Cluster Computing, Journal Year: 2025, Volume and Issue: 28(4)

Published: Feb. 25, 2025

Language: Английский

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Advanced Brain Tumor Classification in MR Images Using Transfer Learning and Pre-Trained Deep CNN Models

Cancers, Journal Year: 2025, Volume and Issue: 17(1), P. 121 - 121

Published: Jan. 2, 2025

Background/Objectives: Brain tumor classification is a crucial task in medical diagnostics, as early and accurate detection can significantly improve patient outcomes. This study investigates the effectiveness of pre-trained deep learning models classifying brain MRI images into four categories: Glioma, Meningioma, Pituitary, No Tumor, aiming to enhance diagnostic process through automation. Methods: A publicly available Tumor dataset containing 7023 was used this research. The employs state-of-the-art models, including Xception, MobileNetV2, InceptionV3, ResNet50, VGG16, DenseNet121, which are fine-tuned using transfer learning, combination with advanced preprocessing data augmentation techniques. Transfer applied fine-tune optimize accuracy while minimizing computational requirements, ensuring efficiency real-world applications. Results: Among tested Xception emerged top performer, achieving weighted 98.73% F1 score 95.29%, demonstrating exceptional generalization capabilities. These proved particularly effective addressing class imbalances delivering consistent performance across various evaluation metrics, thus their suitability for clinical adoption. However, challenges persist improving recall Glioma Meningioma categories, black-box nature requires further attention interpretability trust settings. Conclusions: findings underscore transformative potential imaging, offering pathway toward more reliable, scalable, efficient tools. Future research will focus on expanding diversity, model explainability, validating settings support widespread adoption AI-driven systems healthcare ensure integration workflows.

Language: Английский

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Development of hybrid models based on deep learning and optimized machine learning algorithms for brain tumor Multi-Classification

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 238, P. 122159 - 122159

Published: Oct. 18, 2023

Language: Английский

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Efficient simultaneous segmentation and classification of brain tumors from MRI scans using deep learning

Journal of Applied Biomedicine, Journal Year: 2023, Volume and Issue: 43(3), P. 616 - 633

Published: July 1, 2023

Language: Английский

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Dual Deep CNN for Tumor Brain Classification

Diagnostics, Journal Year: 2023, Volume and Issue: 13(12), P. 2050 - 2050

Published: June 13, 2023

Brain tumor (BT) is a serious issue and potentially deadly disease that receives much attention. However, early detection identification of type location are crucial for effective treatment saving lives. Manual diagnoses time-consuming depend on radiologist experts; the increasing number new cases brain tumors makes it difficult to process massive large amounts data rapidly, as time critical factor in patients' Hence, artificial intelligence (AI) vital understanding its various types. Several studies proposed different techniques BT classification. These machine learning (ML) deep (DL). The ML-based method requires handcrafted or automatic feature extraction algorithms; however, DL becomes superior self-learning robust classification recognition tasks. This research focuses classifying three types using MRI imaging: meningioma, glioma, pituitary tumors. DCTN model depends dual convolutional neural networks with VGG-16 architecture concatenated custom CNN (convolutional networks) architecture. After conducting approximately 22 experiments architectures models, our reached 100% accuracy during training 99% testing. methodology obtained highest possible improvement over existing studies. solution provides revolution healthcare providers can be used future save human

Language: Английский

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A Novel Ensemble Framework for Multi-Classification of Brain Tumors Using Magnetic Resonance Imaging

Diagnostics, Journal Year: 2024, Volume and Issue: 14(4), P. 383 - 383

Published: Feb. 9, 2024

Brain tumors can have fatal consequences, affecting many body functions. For this reason, it is essential to detect brain tumor types accurately and at an early stage start the appropriate treatment process. Although convolutional neural networks (CNNs) are widely used in disease detection from medical images, they face problem of overfitting training phase on limited labeled insufficiently diverse datasets. The existing studies use transfer learning ensemble models overcome these problems. When examined, evident that there a lack weight ratios will be with technique. With framework proposed study, several CNN different architectures trained fine-tuning three A particle swarm optimization-based algorithm determined optimum weights for combining five most successful results across datasets as follows: Dataset 1, 99.35% accuracy 99.20 F1-score; 2, 98.77% 98.92 3, 99.92% 99.92 F1-score. We achieved performances datasets, showing reliable classification. As result, outperforms studies, offering clinicians enhanced decision-making support through its high-accuracy classification performance.

Language: Английский

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Enhancement of brain tumor classification from MRI images using multi-path convolutional neural network with SVM classifier

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 93, P. 106117 - 106117

Published: Feb. 24, 2024

Language: Английский

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