ArticleScientific reports2023
Enhanced brain tumor classification using graph convolutional neural network architecture.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Graph Neural Networks in Neuroimaging: Current Status and Biostatistical Considerations for Clinical Deployment.Annals of biomedical engineering · 2026Review
- Federated MobileNetV2 with ensemble meta-learning for privacy-preserving brain tumor classification.Scientific reports · 2026Article
- Hybrid CNN-GCN framework for brain tumor MRI classification: A graph-based approach to smart healthcare diagnostics.Journal of applied clinical medical physics · 2026Article
- A contrastive learning framework with adaptive feature fusion for brain tumor classification.Scientific reports · 2026Article
- BrainView: A cloud-based deep learning system for brain image segmentation, tumor detection and visualization.Biomedical journal · 2026Article
- Designing implicit population learners: a permutation-equivariant state space approach for brain disease diagnosis.Frontiers in computational neuroscience · 2026Article
- An Efficient Neuro-framework for Brain Tumor Classification Using a CNN-based Self-supervised Learning Approach with Genetic Optimizations.Current neuropharmacology · 2026Article
- A Multi-Task EfficientNetV2S Approach with Hierarchical Hybrid Attention for MRI Enhancing Brain Tumor Segmentation and Classification.Brain sciences · 2025Article
- Brain Tumour Classification Model Based on Spatial Block-Residual Block Collaborative Architecture with Strip Pooling Feature Fusion.Journal of imaging · 2025Article
- Hierarchical multi-scale vision transformer model for accurate detection and classification of brain tumors in MRI-based medical imaging.Scientific reports · 2025Article
- Fine-tuned ResNet34 for efficient brain tumor classification.Scientific reports · 2025Article
- Detection and classification of brain tumor using a hybrid learning model in CT scan images.Scientific reports · 2025Article
- Sparse transformer and multipath decision tree: a novel approach for efficient brain tumor classification.Scientific reports · 2025Article
- Hybrid Attention-Enhanced Xception and Dynamic Chaotic Whale Optimization for Brain Tumor Diagnosis.Bioengineering (Basel, Switzerland) · 2025Article
- Lightweight convolutional neural networks using nonlinear Lévy chaotic moth flame optimisation for brain tumour classification via efficient hyperparameter tuning.Scientific reports · 2025Article
- One solid step to general neuroradiology AI.European radiology · 2025Article
- Nonlinear Harmonics: A Gateway to Enhanced Image Contrast and Material Discrimination.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Brain Tumor Detection and Prediction in MRI Images Utilizing a Fine-Tuned Transfer Learning Model Integrated Within Deep Learning Frameworks.Life (Basel, Switzerland) · 2025Article
- Information Geometry and Manifold Learning: A Novel Framework for Analyzing Alzheimer's Disease MRI Data.Diagnostics (Basel, Switzerland) · 2025Article
- EnSLDe: an enhanced short-range and long-range dependent system for brain tumor classification.Frontiers in oncology · 2025Article
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Authors and funding
7 authors.
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Abstract
The Brain Tumor presents a highly critical situation concerning the brain, characterized by the uncontrolled growth of an abnormal cell cluster. Early brain tumor detection is essential for accurate diagnosis and effective treatment planning. In this paper, a novel Convolutional Neural Network (CNN) based Graph Neural Network (GNN) model is proposed using the publicly available Brain Tumor dataset from Kaggle to predict whether a person has brain tumor or not and if yes then which type (Meningioma, Pituitary or Glioma). The objective of this research and the proposed models is to provide a solution to the non-consideration of non-Euclidean distances in image data and the inability of conventional models to learn on pixel similarity based upon the pixel proximity. To solve this problem, we have proposed a Graph based Convolutional Neural Network (GCNN) model and it is found that the proposed model solves the problem of considering non-Euclidean distances in images. We aimed at improving brain tumor detection and classification using a novel technique which combines GNN and a 26 layered CNN that takes in a Graph input pre-convolved using Graph Convolution operation. The objective of Graph Convolution is to modify the node features (data linked to each node) by combining information from nearby nodes. A standard pre-computed Adjacency matrix is used, and the input graphs were updated as the averaged sum of local neighbor nodes, which carry the regional information about the tumor. These modified graphs are given as the input matrices to a standard 26 layered CNN with Batch Normalization and Dropout layers intact. Five different networks namely Net-0, Net-1, Net-2, Net-3 and Net-4 are proposed, and it is found that Net-2 outperformed the other networks namely Net-0, Net-1, Net-3 and Net-4. The highest accuracy achieved was 95.01% by Net-2. With its current effectiveness, the model we propose represents a critical alternative for the statistical detection of brain tumors in patients who are suspected of having one.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.