ArticleDiagnostics (Basel, Switzerland)2023
Refined Automatic Brain Tumor Classification Using Hybrid Convolutional Neural Networks for MRI Scans.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Advances in artificial intelligence and thermal analysis for brain tumor detection: a review of models, methods, and modalities.Frontiers in artificial intelligence · 2026Review
- A scalable and reliable deep learning framework for enhanced brain tumor detection and diagnosis using AI-based medical imaging.Frontiers in medicine · 2026Article
- Fine-tuned ResNet34 for efficient brain tumor classification.Scientific reports · 2025Article
- Majority Voting Ensemble of Deep CNNs for Robust MRI-Based Brain Tumor Classification.Diagnostics (Basel, Switzerland) · 2025Article
- Review
- Explainable CNN for brain tumor detection and classification through XAI based key features identification.Brain informatics · 2025Article
- XAI-BT-EdgeNet: explainable edge-aware deep learning with squeeze-and-excitation for brain tumor detection and prediction.Frontiers in artificial intelligence · 2025Article
- Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology.Cancers · 2024Review
- Enhancing brain tumor detection: a novel CNN approach with advanced activation functions for accurate medical imaging analysis.Frontiers in oncology · 2024Article
- Deep learning and transfer learning for brain tumor detection and classification.Biology methods & protocols · 2024Article
- Integrating deep learning and thermal estimation for enhanced MRI-based brain tumor diagnosis.Digital healthArticle
Corrections and comments
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Authors and funding
5 authors.
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Abstract
Refined hybrid convolutional neural networks are proposed in this work for classifying brain tumor classes based on MRI scans. A dataset of 2880 T1-weighted contrast-enhanced MRI brain scans are used. The dataset contains three main classes of brain tumors: gliomas, meningiomas, and pituitary tumors, as well as a class of no tumors. Firstly, two pre-trained, fine-tuned convolutional neural networks, GoogleNet and AlexNet, were used for classification process, with validation and classification accuracy being 91.5% and 90.21%, respectively. Then, to improving the performance of the fine-tuning AlexNet, two hybrid networks (AlexNet-SVM and AlexNet-KNN) were applied. These hybrid networks achieved 96.9% and 98.6% validation and accuracy, respectively. Thus, the hybrid network AlexNet-KNN was shown to be able to apply the classification process of the present data with high accuracy. After exporting these networks, a selected dataset was employed for testing process, yielding accuracies of 88%, 85%, 95%, and 97% for the fine-tuned GoogleNet, the fine-tuned AlexNet, AlexNet-SVM, and AlexNet-KNN, respectively. The proposed system would help for automatic detection and classification of the brain tumor from the MRI scans and safe the time for the clinical diagnosis.
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