ArticleScientific reports2024
Brain tumor detection and classification in MRI using hybrid ViT and GRU model with explainable AI in Southern Bangladesh.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- A Hybrid Approach for Brain-Tumor Detection and Classification from MRI Images.Journal of imaging · 2026Article
- Brain Tumor Classification Using Convolutional Neural Network and Bitterling Fish Optimization Algorithm.Diagnostics (Basel, Switzerland) · 2026Article
- Enhancing brain tumor detection through deep learning and explainable AI techniques.Scientific reports · 2026Article
- ETDACVO: Structural-Fidelity-Aware Evolutionary Co-Optimization for Robust and Explainable Brain Tumor MRI Classification.Biomedicines · 2026Article
- A Dual-Branch Frequency-Aware Attention Framework for Rare Neurological Disease Classification from Brain MRI.Diagnostics (Basel, Switzerland) · 2026Article
- Proto-memformer: deformable memory transformer for Parkinson's MRI classification.Scientific reports · 2026Article
- BrainFusionNet: a deep learning and XAI model to understand local, global, and sequential features of MRI images for improved brain tumour detection.Brain informatics · 2026Article
- BrainGraphNet-ViT model: a hybrid model combining vision transformers and graph convolutional networks for brain tumor diagnosis.Scientific reports · 2026Article
- Brain tumor classification using optimized ResNet50 with dynamic precision optimization for enhanced speed and diagnostic accuracy.Scientific reports · 2026Article
- Role of matrix metalloproteinases in the invasion of glioblastoma and drug interventions (Review).International journal of molecular medicine · 2026Review
- A Robust ConvNeXt-Based Framework for Efficient, Generalizable, and Explainable Brain Tumor Classification on MRI.Bioengineering (Basel, Switzerland) · 2026Article
- Advances in artificial intelligence and thermal analysis for brain tumor detection: a review of models, methods, and modalities.Frontiers in artificial intelligence · 2026Review
- Unveiling the non-linear synergistic effects of smoking and aging on cataract-AMD comorbidity: an explainable artificial intelligence approach.Frontiers in public health · 2026Article
- Development of an AI-based magnetic resonance imaging reading support program (AMP) for deep endometriosis diagnosis.Scientific reports · 2025Article
- Few-Shot and Zero-Shot Learning for MRI Brain Tumor Classification Using CLIP and Vision Transformers.Sensors (Basel, Switzerland) · 2025Article
- Hybrid deep learning framework for cardiovascular disease diagnosis and prognosis using GAN, LSTM, GRU, VARMA, and deep DynaQ network.Scientific reports · 2025Article
- Multi-stage knowledge distillation with layer fusion-based deep learning approach for skin cancer classification.Scientific reports · 2025Article
- Hierarchical multi-scale vision transformer model for accurate detection and classification of brain tumors in MRI-based medical imaging.Scientific reports · 2025Article
- Artificial Intelligence in the Diagnosis and Treatment of Brain Gliomas.Biomedicines · 2025Review
- Evaluation of Apical Closure in Panoramic Radiographs Using Vision Transformer Architectures ViT-Based Apical Closure Classification.Diagnostics (Basel, Switzerland) · 2025Article
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11 authors.
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Abstract
Brain tumor, a leading cause of uncontrolled cell growth in the central nervous system, presents substantial challenges in medical diagnosis and treatment. Early and accurate detection is essential for effective intervention. This study aims to enhance the detection and classification of brain tumors in Magnetic Resonance Imaging (MRI) scans using an innovative framework combining Vision Transformer (ViT) and Gated Recurrent Unit (GRU) models. We utilized primary MRI data from Bangabandhu Sheikh Mujib Medical College Hospital (BSMMCH) in Faridpur, Bangladesh. Our hybrid ViT-GRU model extracts essential features via ViT and identifies relationships between these features using GRU, addressing class imbalance and outperforming existing diagnostic methods. We extensively processed the dataset, and then trained the model using various optimizers (SGD, Adam, AdamW) and evaluated through rigorous 10-fold cross-validation. Additionally, we incorporated Explainable Artificial Intelligence (XAI) techniques-Attention Map, SHAP, and LIME-to enhance the interpretability of the model's predictions. For the primary dataset BrTMHD-2023, the ViT-GRU model achieved precision, recall, and F1-score metrics of 97%. The highest accuracies obtained with SGD, Adam, and AdamW optimizers were 81.66%, 96.56%, and 98.97%, respectively. Our model outperformed existing Transfer Learning models by 1.26%, as validated through comparative analysis and cross-validation. The proposed model also shows excellent performances with another Brain Tumor Kaggle Dataset outperforming the existing research done on the same dataset with 96.08% accuracy. The proposed ViT-GRU framework significantly improves the detection and classification of brain tumors in MRI scans. The integration of XAI techniques enhances the model's transparency and reliability, fostering trust among clinicians and facilitating clinical application. Future work will expand the dataset and apply findings to real-time diagnostic devices, advancing the field.
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