ArticleEntropy (Basel, Switzerland)2022
A Hybrid Deep Learning Model for Brain Tumour Classification.
Article in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 171 citations in OpenAlex.
- A systematic review of the hybrid machine learning models for brain tumour segmentation and detection in medical images.Frontiers in artificial intelligence · 2025Pooled it
- A Class-Wise Deep Ensemble Framework Using ResNet101 and DenseNet201 for Brain Tumor Classification.Journal of imaging informatics in medicine · 2026Article
- Explainable Hybrid Deep Learning Framework Integrating MobileNetV2, EfficientNetV2B0, and KNN for MRI-Based Brain Tumor Classification.Cellular and molecular neurobiology · 2026Article
- Machine learning in cancer imaging for enhanced precision in diagnosis and therapy.Discover computing · 2026Review
- Optimized deep learning for brain tumor detection: a hybrid approach with attention mechanisms and clinical explainability.Scientific reports · 2025Article
- AI-Driven Innovations in Neuroradiology and Neurosurgery: Scoping Review of Current Evidence and Future Directions.Cancers · 2025Review
- Automatic smart brain tumor classification and prediction system using deep learning.Scientific reports · 2025Article
- Automated multi-class MRI brain tumor classification and segmentation using deformable attention and saliency mapping.Scientific reports · 2025Article
- Enhancing privacy-preserving brain tumor classification with adaptive reputation-aware federated learning and homomorphic encryption.PeerJ. Computer science · 2025Article
- Leveraging a hybrid convolutional gated recursive diabetes prediction and severity grading model through a mobile app.PeerJ. Computer science · 2025Article
- Efficient and Accurate Brain Tumor Classification Using Hybrid MobileNetV2-Support Vector Machine for Magnetic Resonance Imaging Diagnostics in Neoplasms.Brain sciences · 2024Article
- Enhancing multiclass brain tumor diagnosis using SVM and innovative feature extraction techniques.Scientific reports · 2024Article
- Enhancing brain tumor classification through ensemble attention mechanism.Scientific reports · 2024Article
- A Novel Ensemble Framework for Multi-Classification of Brain Tumors Using Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2024Article
- Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology.Cancers · 2024Review
- A Systematic Review of Real-Time Deep Learning Methods for Image-Based Cancer Diagnostics.Journal of multidisciplinary healthcare · 2024Article
- MLR-predictor: a versatile and efficient computational framework for multi-label requirements classification.Frontiers in artificial intelligence · 2024Article
- Enhancing Prediction of Brain Tumor Classification Using Images and Numerical Data Features.Diagnostics (Basel, Switzerland) · 2023Article
- PatchResNet: Multiple Patch Division-Based Deep Feature Fusion Framework for Brain Tumor Classification Using MRI Images.Journal of digital imaging · 2023Article
- Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 3 countries.
Funding
Abstract
A brain tumour is one of the major reasons for death in humans, and it is the tenth most common type of tumour that affects people of all ages. However, if detected early, it is one of the most treatable types of tumours. Brain tumours are classified using biopsy, which is not usually performed before definitive brain surgery. An image classification technique for tumour diseases is important for accelerating the treatment process and avoiding surgery and errors from manual diagnosis by radiologists. The advancement of technology and machine learning (ML) can assist radiologists in tumour diagnostics using magnetic resonance imaging (MRI) images without invasive procedures. This work introduced a new hybrid CNN-based architecture to classify three brain tumour types through MRI images. The method suggested in this paper uses hybrid deep learning classification based on CNN with two methods. The first method combines a pre-trained Google-Net model of the CNN algorithm for feature extraction with SVM for pattern classification. The second method integrates a finely tuned Google-Net with a soft-max classifier. The proposed approach was evaluated using MRI brain images that contain a total of 1426 glioma images, 708 meningioma images, 930 pituitary tumour images, and 396 normal brain images. The reported results showed that an accuracy of 93.1% was achieved from the finely tuned Google-Net model. However, the synergy of Google-Net as a feature extractor with an SVM classifier improved recognition accuracy to 98.1%.
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What OpenQuestion holds
Registered trials
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.