ArticleBrain sciences2023
Automated Classification of Brain Tumors from Magnetic Resonance Imaging Using Deep Learning.
Article in Brain sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance of deep learning models for automatic histopathological grading of meningiomas: a systematic review and meta-analysis.Frontiers in neurology · 2025Pooled it
- Research progress of deep learning based on magnetic resonance imaging in meningioma.Magma (New York, N.Y.) · 2026Review
- Multi-class classification of brain tumors using optimized CNN and transfer learning techniques.Scientific reports · 2026Article
- Retrospection dropout bare-bones particle swarm optimization for feature-based brain tumor classification in MRI images.Frontiers in oncology · 2026Article
- NeuroTrustNet: a cost-effective multimodal ensemble framework for brain tumor classification under cross-dataset variability.Frontiers in artificial intelligence · 2026Article
- Enhanced brain tumor segmentation in medical imaging using multi-modal multi-scale contextual aggregation and attention fusion.Scientific reports · 2025Article
- AI-Driven Innovations in Neuroradiology and Neurosurgery: Scoping Review of Current Evidence and Future Directions.Cancers · 2025Review
- Advanced Deep Learning and Machine Learning Techniques for MRI Brain Tumor Analysis: A Review.Sensors (Basel, Switzerland) · 2025Review
- Automated brain tumor recognition using equilibrium optimizer with deep learning approach on MRI images.Scientific reports · 2024Article
- Brain tumor detection and classification in MRI using hybrid ViT and GRU model with explainable AI in Southern Bangladesh.Scientific reports · 2024Article
- High-Precision Skin Disease Diagnosis through Deep Learning on Dermoscopic Images.Bioengineering (Basel, Switzerland) · 2024Article
- Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology.Cancers · 2024Review
- Efficient Skip Connections-Based Residual Network (ESRNet) for Brain Tumor Classification.Diagnostics (Basel, Switzerland) · 2023Article
- Brain Tumor Classification from MRI Using Image Enhancement and Convolutional Neural Network Techniques.Brain sciences · 2023Article
- Brain tumor classification using fine-tuned transfer learning models on magnetic resonance imaging (MRI) images.Digital healthArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain tumor classification is crucial for medical evaluation in computer-assisted diagnostics (CAD). However, manual diagnosis of brain tumors from magnetic resonance imaging (MRI) can be time-consuming and complex, leading to inaccurate detection and classification. This is mainly because brain tumor identification is a complex procedure that relies on different modules. The advancements in Deep Learning (DL) have assisted in the automated process of medical images and diagnostics for various medical conditions, which benefits the health sector. Convolutional Neural Network (CNN) is one of the most prominent DL methods for visual learning and image classification tasks. This study presents a novel CNN algorithm to classify the brain tumor types of glioma, meningioma, and pituitary. The algorithm was tested on benchmarked data and compared with the existing pre-trained VGG16, VGG19, ResNet50, MobileNetV2, and InceptionV3 algorithms reported in the literature. The experimental results have indicated a high classification accuracy of 98.04%, precision, recall, and f1-score success rate of 98%, respectively. The classification results proved that the most common kinds of brain tumors could be categorized with a high level of accuracy. The presented algorithm has good generalization capability and execution speed that can be helpful in the field of medicine to assist doctors in making prompt and accurate decisions associated with brain tumor diagnosis.
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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.