ArticleBioengineering (Basel, Switzerland)2024
Integrating Convolutional Neural Networks with Attention Mechanisms for Magnetic Resonance Imaging-Based Classification of Brain Tumors.
Article in Bioengineering (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Brain tumor segmentation using dual-stream multiscale 3D-UNET with dense net and spatial attention.Scientific reports · 2026Article
- Optimized High- Resolution Network for Accurate Facial Wrinkle Detection Using Harbor Seal Whiskers Optimization.Biomedical engineering and computational biology · 2026Article
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- MedSpectralNet: A lightweight convolutional neural network architecture for multi-modal image classification.PloS one · 2026Article
- Robust epileptic seizure prediction: A 3D-SERESNet framework for patient-specific and multi-patient generalization.iScience · 2025Article
- Enhanced brain tumor segmentation in medical imaging using multi-modal multi-scale contextual aggregation and attention fusion.Scientific reports · 2025Article
- A Web-Deployed, Explainable AI System for Comprehensive Brain Tumor Diagnosis.Neurology international · 2025Article
- DTCNet: finger flexion decoding with three-dimensional ECoG data.Frontiers in computational neuroscience · 2025Article
- Integrative multi-stage deep learning framework for ovarian tumor ultrasound classification with explainability and confidence estimation.Frontiers in medicine · 2025Article
- Dynamic Focus on Tumor Boundaries: A Lightweight U-Net for MRI Brain Tumor Segmentation.Bioengineering (Basel, Switzerland) · 2024Article
- UV Hyperspectral Imaging with Xenon and Deuterium Light Sources: Integrating PCA and Neural Networks for Analysis of Different Raw Cotton Types.Journal of imaging · 2024Article
- Accelerating Brain MR Imaging With Multisequence and Convolutional Neural Networks.Brain and behavior · 2024Article
- Convolutional Neural Network Incorporating Multiple Attention Mechanisms for MRI Classification of Lumbar Spinal Stenosis.Bioengineering (Basel, Switzerland) · 2024Article
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Authors and funding
6 authors.
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Abstract
The application of magnetic resonance imaging (MRI) in the classification of brain tumors is constrained by the complex and time-consuming characteristics of traditional diagnostics procedures, mainly because of the need for a thorough assessment across several regions. Nevertheless, advancements in deep learning (DL) have facilitated the development of an automated system that improves the identification and assessment of medical images, effectively addressing these difficulties. Convolutional neural networks (CNNs) have emerged as steadfast tools for image classification and visual perception. This study introduces an innovative approach that combines CNNs with a hybrid attention mechanism to classify primary brain tumors, including glioma, meningioma, pituitary, and no-tumor cases. The proposed algorithm was rigorously tested with benchmark data from well-documented sources in the literature. It was evaluated alongside established pre-trained models such as Xception, ResNet50V2, Densenet201, ResNet101V2, and DenseNet169. The performance metrics of the proposed method were remarkable, demonstrating classification accuracy of 98.33%, precision and recall of 98.30%, and F1-score of 98.20%. The experimental finding highlights the superior performance of the new approach in identifying the most frequent types of brain tumors. Furthermore, the method shows excellent generalization capabilities, making it an invaluable tool for healthcare in diagnosing brain conditions accurately and efficiently.
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