ArticleScientific reports2025
A hybrid explainable model based on advanced machine learning and deep learning models for classifying brain tumors using MRI images.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- MRI-based brain tumor detection through an explainable EfficientNetV2 and MLP-mixer attention architecture.Physical and engineering sciences in medicine · 2026Article
- A hybrid Wavelet-EfficientNetV2-Vision transformer framework for multiclass brain tumor MRI classification.Physical and engineering sciences in medicine · 2026Article
- A Class-Wise Deep Ensemble Framework Using ResNet101 and DenseNet201 for Brain Tumor Classification.Journal of imaging informatics in medicine · 2026Article
- Advanced Deep Learning Architectures in MRI-Based Brain Tumor Classification: A Systematic Review Focused on Meningiomas.Journal of imaging informatics in medicine · 2026Review
- A multi-level attention CNN-transformer based framework for the detection of brain tumor using regional dual-score explainability.Scientific reports · 2026Article
- An explainable multi-stage framework for brain tumor classification using hybrid feature fusion and EfficientNetB5 model.Scientific reports · 2026Article
- A minimal-net CNN model for an IoT-based brain tumor detection and monitoring system.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
- QuantumNeuroXAI: a quantum-inspired deep learning framework with explainability for brain signal analysis and neurological disorder detection.Scientific reports · 2026Article
- MRI-based brain tumor prediction using convolutional neural network framework.Scientific reports · 2026Article
- A Robust ConvNeXt-Based Framework for Efficient, Generalizable, and Explainable Brain Tumor Classification on MRI.Bioengineering (Basel, Switzerland) · 2026Article
- Global-local feature fusion: a robust hybrid deep learning model for multiclass brain tumor classification with Grad-CAM++ interpretation.Frontiers in genetics · 2026Article
- NeuroTrustNet: a cost-effective multimodal ensemble framework for brain tumor classification under cross-dataset variability.Frontiers in artificial intelligence · 2026Article
- XcepFusion for brain tumor detection using a hybrid transfer learning framework with layer pruning and freezing.Scientific reports · 2025Article
- Interpretable ensemble learning for tumor-type prediction with a SHAP-based evaluation of CatBoost and voting classifiers.Scientific reports · 2025Article
- AI-based neoadjuvant immunotherapy response prediction across pan-cancer: a comprehensive review.Cancer cell international · 2025Review
- Microwave head imaging systems for early brain tumor detection: antenna designs and emerging substrates.MethodsX · 2025Review
- Enhanced brain tumor segmentation in medical imaging using multi-modal multi-scale contextual aggregation and attention fusion.Scientific reports · 2025Article
- Transparent brain tumor detection using DenseNet169 and LIME.Scientific reports · 2025Article
- Majority Voting Ensemble of Deep CNNs for Robust MRI-Based Brain Tumor Classification.Diagnostics (Basel, Switzerland) · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain tumors present a significant global health challenge, and their early detection and accurate classification are crucial for effective treatment strategies. This study presents a novel approach combining a lightweight parallel depthwise separable convolutional neural network (PDSCNN) and a hybrid ridge regression extreme learning machine (RRELM) for accurately classifying four types of brain tumors (glioma, meningioma, no tumor, and pituitary) based on MRI images. The proposed approach enhances the visibility and clarity of tumor features in MRI images by employing contrast-limited adaptive histogram equalization (CLAHE). A lightweight PDSCNN is then employed to extract relevant tumor-specific patterns while minimizing computational complexity. A hybrid RRELM model is proposed, enhancing the traditional ELM for improved classification performance. The proposed framework is compared with various state-of-the-art models in terms of classification accuracy, model parameters, and layer sizes. The proposed framework achieved remarkable average precision, recall, and accuracy values of 99.35%, 99.30%, and 99.22%, respectively, through five-fold cross-validation. The PDSCNN-RRELM outperformed the extreme learning machine model with pseudoinverse (PELM) and exhibited superior performance. The introduction of ridge regression in the ELM framework led to significant enhancements in classification performance model parameters and layer sizes compared to those of the state-of-the-art models. Additionally, the interpretability of the framework was demonstrated using Shapley Additive Explanations (SHAP), providing insights into the decision-making process and increasing confidence in real-world diagnosis.
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