ArticleDiagnostics (Basel, Switzerland)2023
Dual Deep CNN for Tumor Brain Classification.
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 18 papers.
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Who cites it
18 citing papers in PubMed.
- Advanced Deep Learning Architectures in MRI-Based Brain Tumor Classification: A Systematic Review Focused on Meningiomas.Journal of imaging informatics in medicine · 2026Review
- A minimal-net CNN model for an IoT-based brain tumor detection and monitoring system.Scientific reports · 2026Article
- Multi-class classification of brain tumors using optimized CNN and transfer learning techniques.Scientific reports · 2026Article
- Interpretability and performance of a 3D C-vit model for accurate grading of pediatric brain tumors.Frontiers in oncology · 2026Article
- A deep ensemble learning framework for brain tumor classification using data balancing and fine-tuning.Scientific reports · 2025Article
- Explainable Deep Ensemble Meta-Learning Framework for Brain Tumor Classification Using MRI Images.Cancers · 2025Article
- Deep learning-driven brain tumor classification and segmentation using non-contrast MRI.Scientific reports · 2025Article
- A Radiomic Model for Gliomas Grade and Patient Survival Prediction.Bioengineering (Basel, Switzerland) · 2025Article
- Hydrogel-based nanoparticles: revolutionizing brain tumor treatment and paving the way for future innovations.European journal of medical research · 2025Review
- A novel approach for the detection of brain tumor and its classification via end-to-end vision transformer - CNN architecture.Frontiers in oncology · 2025Article
- XAI-MRI: an ensemble dual-modality approach for 3D brain tumor segmentation using magnetic resonance imaging.Frontiers in artificial intelligence · 2025Article
- A novel brain tumor magnetic resonance imaging dataset (Gazi Brains 2020): initial benchmark results and comprehensive analysis.PeerJ. Computer science · 2025Article
- Brain tumor diagnosis in MRI scans images using Residual/Shuffle Network optimized by augmented Falcon Finch optimization.Scientific reports · 2024Article
- Enhancing multiclass brain tumor diagnosis using SVM and innovative feature extraction techniques.Scientific reports · 2024Article
- Article
- A hybrid deep CNN model for brain tumor image multi-classification.BMC medical imaging · 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
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain tumor (BT) is a serious issue and potentially deadly disease that receives much attention. However, early detection and identification of tumor type and location are crucial for effective treatment and saving lives. Manual diagnoses are time-consuming and depend on radiologist experts; the increasing number of new cases of brain tumors makes it difficult to process massive and large amounts of data rapidly, as time is a critical factor in patients' lives. Hence, artificial intelligence (AI) is vital for understanding disease and its various types. Several studies proposed different techniques for BT detection and classification. These studies are on machine learning (ML) and deep learning (DL). The ML-based method requires handcrafted or automatic feature extraction algorithms; however, DL becomes superior in self-learning and robust in classification and recognition tasks. This research focuses on classifying three types of tumors using MRI imaging: meningioma, glioma, and pituitary tumors. The proposed DCTN model depends on dual convolutional neural networks with VGG-16 architecture concatenated with custom CNN (convolutional neural networks) architecture. After conducting approximately 22 experiments with different architectures and models, our model reached 100% accuracy during training and 99% during testing. The proposed methodology obtained the highest possible improvement over existing research studies. The solution provides a revolution for healthcare providers that can be used as a different disease classification in the future and save human lives.
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Registered trials
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