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

Neurocomputing, Год журнала: 2024, Номер 573, С. 127216 - 127216

Опубликована: Янв. 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.

Язык: Английский

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

Cluster Computing, Год журнала: 2025, Номер 28(4)

Опубликована: Фев. 25, 2025

Язык: 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, Год журнала: 2025, Номер unknown, С. 104025 - 104025

Опубликована: Янв. 1, 2025

Язык: 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, Год журнала: 2025, Номер unknown, С. 104327 - 104327

Опубликована: Фев. 1, 2025

Язык: Английский

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

Cancers, Год журнала: 2025, Номер 17(1), С. 121 - 121

Опубликована: Янв. 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.

Язык: 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, Год журнала: 2023, Номер 238, С. 122159 - 122159

Опубликована: Окт. 18, 2023

Язык: Английский

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

Journal of Applied Biomedicine, Год журнала: 2023, Номер 43(3), С. 616 - 633

Опубликована: Июль 1, 2023

Язык: Английский

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Dual Deep CNN for Tumor Brain Classification

Diagnostics, Год журнала: 2023, Номер 13(12), С. 2050 - 2050

Опубликована: Июнь 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

Язык: Английский

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

Diagnostics, Год журнала: 2024, Номер 14(4), С. 383 - 383

Опубликована: Фев. 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.

Язык: 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, Год журнала: 2024, Номер 93, С. 106117 - 106117

Опубликована: Фев. 24, 2024

Язык: Английский

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