ArticleScientific reports2024
NeuroNet19: an explainable deep neural network model for the classification of brain tumors using magnetic resonance imaging data.
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 21 papers.
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21 citing papers in PubMed.
- A Hybrid Approach for Brain-Tumor Detection and Classification from MRI Images.Journal of imaging · 2026Article
- Research progress of deep learning based on magnetic resonance imaging in meningioma.Magma (New York, N.Y.) · 2026Review
- Lightweight Transfer Learning Models for Multi-Class Brain Tumor Classification: Glioma, Meningioma, Pituitary Tumors, and No Tumor MRI Screening.Journal of imaging informatics in medicine · 2026Article
- CAHA-Net: A novel MR image classification model based on DenseNet incorporating coordinate attention and hybrid augmentation.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
- BrainView: A cloud-based deep learning system for brain image segmentation, tumor detection and visualization.Biomedical journal · 2026Article
- NeuroTrustNet: a cost-effective multimodal ensemble framework for brain tumor classification under cross-dataset variability.Frontiers in artificial intelligence · 2026Article
- Fine-tuned ResNet34 for efficient brain tumor classification.Scientific reports · 2025Article
- Brain Tumor Classification in MRI Scans Using Edge Computing and a Shallow Attention-Guided CNN.Biomedicines · 2025Article
- DCNN models with post-hoc interpretability for the automated detection of glossitis and OSCC on the tongue.Scientific reports · 2025Article
- A Hybrid Model of Feature Extraction and Dimensionality Reduction Using ViT, PCA, and Random Forest for Multi-Classification of Brain Cancer.Diagnostics (Basel, Switzerland) · 2025Article
- Advanced Deep Learning and Machine Learning Techniques for MRI Brain Tumor Analysis: A Review.Sensors (Basel, Switzerland) · 2025Review
- The clinical implications and interpretability of computational medical imaging (radiomics) in brain tumors.Insights into imaging · 2025Review
- Dilated SE-DenseNet for brain tumor MRI classification.Scientific reports · 2025Article
- Explainable AI in Diagnostic Radiology for Neurological Disorders: A Systematic Review, and What Doctors Think About It.Diagnostics (Basel, Switzerland) · 2025Review
- Article
- Convolutional Neural Network-Machine Learning Model: Hybrid Model for Meningioma Tumour and Healthy Brain Classification.Journal of imaging · 2024Article
- Enhancing Deep Learning Model Explainability in Brain Tumor Datasets Using Post-Heuristic Approaches.Journal of imaging · 2024Article
- DDFC: deep learning approach for deep feature extraction and classification of brain tumors using magnetic resonance imaging in E-healthcare system.Scientific reports · 2024Article
- Article
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4 authors.
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Abstract
Brain tumors (BTs) are one of the deadliest diseases that can significantly shorten a person's life. In recent years, deep learning has become increasingly popular for detecting and classifying BTs. In this paper, we propose a deep neural network architecture called NeuroNet19. It utilizes VGG19 as its backbone and incorporates a novel module named the Inverted Pyramid Pooling Module (iPPM). The iPPM captures multi-scale feature maps, ensuring the extraction of both local and global image contexts. This enhances the feature maps produced by the backbone, regardless of the spatial positioning or size of the tumors. To ensure the model's transparency and accountability, we employ Explainable AI. Specifically, we use Local Interpretable Model-Agnostic Explanations (LIME), which highlights the features or areas focused on while predicting individual images. NeuroNet19 is trained on four classes of BTs: glioma, meningioma, no tumor, and pituitary tumors. It is tested on a public dataset containing 7023 images. Our research demonstrates that NeuroNet19 achieves the highest accuracy at 99.3%, with precision, recall, and F1 scores at 99.2% and a Cohen Kappa coefficient (CKC) of 99%.
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