ArticleScientific reports2025
T1-weighted MRI-based brain tumor classification using hybrid deep learning models.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- A hybrid deep learning framework based on VGG19 and U-Net for accurate brain tumor segmentation in MRI images.Magnetic resonance letters · 2026Article
- Brain Tumor Segmentation and Grading on MRI Using Deep Learning: A Systematic Literature Review and Benchmark-Driven Comparative Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- CAE-BrainNet: a statistically validated class-adaptive attention ensemble model for explainable brain tumor classification from MRI.Brain informatics · 2026Article
- Hybrid fractal ConvXGBNet for brain tumor classification with log-cosh dice loss - cascaded V-Net based segmentation based on MRI images.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- An explainable multi-stage framework for brain tumor classification using hybrid feature fusion and EfficientNetB5 model.Scientific reports · 2026Article
- Clinical validation of lightweight CNN architectures for reliable multi-class classification of lung cancer using histopathological imaging techniques.Scientific reports · 2026Article
- Skeletal Muscle Mass Estimation from Lower Leg Digital Images Using Machine Learning.Progress in rehabilitation medicine · 2026Article
- Convolutional neural networks in brain disease diagnosis: a unified review of Alzheimer's, Parkinson's, and brain tumor classification.Frontiers in neuroscience · 2026Review
- Brain tumor detection with real-world predictions in Jordan hospitals.Scientific reports · 2025Article
- Interpretable ensemble learning for tumor-type prediction with a SHAP-based evaluation of CatBoost and voting classifiers.Scientific reports · 2025Article
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- From Anatomy to Genomics Using a Multi-Task Deep Learning Approach for Comprehensive Glioma Profiling.Bioengineering (Basel, Switzerland) · 2025Article
- Pyramidal attention-based T network for brain tumor classification: a comprehensive analysis of transfer learning approaches for clinically reliable and reliable AI hybrid approaches.Scientific reports · 2025Article
- A novel deep learning approach for predicting stone-free rates post-ESWL on uncontrasted CT.PeerJ. Computer science · 2025Article
- Transfer deep learning and explainable AI framework for brain tumor and Alzheimer's detection across multiple datasets.Frontiers in medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Health is fundamental to human well-being, with brain health particularly critical for cognitive functions. Magnetic resonance imaging (MRI) serves as a cornerstone in diagnosing brain health issues, providing essential data for healthcare decisions. These images represent vast datasets that are increasingly harnessed by deep learning for high-performance image processing and classification tasks. In our study, we focus on classifying brain tumors-such as glioma, meningioma, and pituitary tumors-using the U-Net architecture applied to MRI scans. Additionally, we explore the effectiveness of convolutional neural networks including Inception-V3, EfficientNetB4, and VGG19, augmented through transfer learning techniques. Evaluation metrics such as F-score, recall, precision, and accuracy were employed to assess model performance. The U-Net segmentation architecture, emerged as the top performer, achieving an accuracy of 98.56%, along with an F-score of 99%, an area under the curve of 99.8%, and recall and precision rates of 99%. This study demonstrates the effectiveness of U-Net, a convolutional neural network architecture, for accurate brain tumor segmentation in early detection and treatment planning. Achieving an accuracy of 96.01% in cross-dataset validation with an external cohort, U-Net exhibited robust performance across diverse clinical scenarios. Our findings highlight the potential of U-Net and transfer learning in enhancing diagnostic accuracy and informing clinical decision-making in neuroimaging, ultimately improving patient care and outcomes.
Indexed as
Identifiers
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.