ArticleBMC medical imaging2024
A hybrid deep CNN model for brain tumor image multi-classification.
Article in BMC medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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Who cites it
35 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
- A Hybrid Approach for Brain-Tumor Detection and Classification from MRI Images.Journal of imaging · 2026Article
- A novel hybrid transformer-based framework (H-ConvNeXt-Swin) to classify brain tumors using MRI.Scientific reports · 2026Article
- AGTS-Net: Anatomically Guided Two-Stage Network for Actionable Stroke Classification in Complete NCCT Studies.Bioengineering (Basel, Switzerland) · 2026Article
- Segmentation-Free Preoperative 3D MRI Classification of Low-Grade Versus High-Grade Glioma Using Task-Oriented Neural Architecture Search.Journal of imaging · 2026Article
- Convolutional Neural Networks in Radiology: Principles, Clinical Applications, and a Practical Framework for Radiologists.Medical principles and practice : international journal of the Kuwait University, Health Science Centre · 2026Review
- Hierarchical Deep Learning Framework for Skin Disease and Cancer Classification Performance Enhancement.Sensors (Basel, Switzerland) · 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
- Hybrid Aquila optimizer-Harris Hawks optimization for CNN hyperparameter tuning in brain tumor classification.Scientific reports · 2026Article
- A Robust ConvNeXt-Based Framework for Efficient, Generalizable, and Explainable Brain Tumor Classification on MRI.Bioengineering (Basel, Switzerland) · 2026Article
- Convolutional neural networks in brain disease diagnosis: a unified review of Alzheimer's, Parkinson's, and brain tumor classification.Frontiers in neuroscience · 2026Review
- SMBO: a Sinusoidal Modulation Bi-population Optimizer for brain cancer MRI image clustering.Frontiers in neuroscience · 2026Article
- XcepFusion for brain tumor detection using a hybrid transfer learning framework with layer pruning and freezing.Scientific reports · 2025Article
- An intelligent brain tumor detection model using lightweight hybrid twin attentive pyramid convolutional network.Scientific reports · 2025Article
- Hierarchical multi-scale vision transformer model for accurate detection and classification of brain tumors in MRI-based medical imaging.Scientific reports · 2025Article
- Development of a Fully Optimized Convolutional Neural Network for Astrocytoma Classification in MRI Using Explainable Artificial Intelligence.Journal of imaging · 2025Article
- Evaluation of Helmet Wearing Compliance: A Bionic Spidersense System-Based Method for Helmet Chinstrap Detection.Biomimetics (Basel, Switzerland) · 2025Article
- Radiomics early assessment of post chemotherapy cardiotoxicity in cancer patients using 2D echocardiography imaging an interpretable machine learning study.Scientific reports · 2025Article
- Advanced dynamic ensemble framework with explainability driven insights for precision brain tumor classification across datasets.Scientific reports · 2025Article
- Sparse transformer and multipath decision tree: a novel approach for efficient brain tumor classification.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The current approach to diagnosing and classifying brain tumors relies on the histological evaluation of biopsy samples, which is invasive, time-consuming, and susceptible to manual errors. These limitations underscore the pressing need for a fully automated, deep-learning-based multi-classification system for brain malignancies. This article aims to leverage a deep convolutional neural network (CNN) to enhance early detection and presents three distinct CNN models designed for different types of classification tasks. The first CNN model achieves an impressive detection accuracy of 99.53% for brain tumors. The second CNN model, with an accuracy of 93.81%, proficiently categorizes brain tumors into five distinct types: normal, glioma, meningioma, pituitary, and metastatic. Furthermore, the third CNN model demonstrates an accuracy of 98.56% in accurately classifying brain tumors into their different grades. To ensure optimal performance, a grid search optimization approach is employed to automatically fine-tune all the relevant hyperparameters of the CNN models. The utilization of large, publicly accessible clinical datasets results in robust and reliable classification outcomes. This article conducts a comprehensive comparison of the proposed models against classical models, such as AlexNet, DenseNet121, ResNet-101, VGG-19, and GoogleNet, reaffirming the superiority of the deep CNN-based approach in advancing the field of brain tumor classification and early detection.
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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.