ArticleScientific reports2024
DDFC: deep learning approach for deep feature extraction and classification of brain tumors using magnetic resonance imaging in E-healthcare system.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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Who cites it
8 citing papers in PubMed, 28 citations in OpenAlex.
- A multi-level attention CNN-transformer based framework for the detection of brain tumor using regional dual-score explainability.Scientific reports · 2026Article
- Convolutional neural networks in brain disease diagnosis: a unified review of Alzheimer's, Parkinson's, and brain tumor classification.Frontiers in neuroscience · 2026Review
- The impact of deep learning and omics data in transforming precision therapy for brain cancer.Frontiers in bioinformatics · 2026Review
- Enhanced brain tumor segmentation in medical imaging using multi-modal multi-scale contextual aggregation and attention fusion.Scientific reports · 2025Article
- Accurate and real-time brain tumour detection and classification using optimized YOLOv5 architecture.Scientific reports · 2025Article
- Developing an innovative lung cancer detection model for accurate diagnosis in AI healthcare systems.Scientific reports · 2025Article
- Advanced Deep Learning and Machine Learning Techniques for MRI Brain Tumor Analysis: A Review.Sensors (Basel, Switzerland) · 2025Review
- MRI based early Temporal Lobe Epilepsy detection using DGWO based optimized HAETN and Fuzzy-AAL Segmentation Framework (FASF).PloS one · 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
7 authors at 4 institutions in 4 countries.
Funding
Abstract
This research explores the use of gated recurrent units (GRUs) for automated brain tumor detection using MRI data. The GRU model captures sequential patterns and considers spatial information within individual MRI images and the temporal evolution of lesion characteristics. The proposed approach improves the accuracy of tumor detection using MRI images. The model's performance is benchmarked against conventional CNNs and other recurrent architectures. The research addresses interpretability concerns by employing attention mechanisms that highlight salient features contributing to the model's decisions. The proposed model attention-gated recurrent units (A-GRU) results show promising results, indicating that the proposed model surpasses the state-of-the-art models in terms of accuracy and obtained 99.32% accuracy. Due to the high predictive capability of the proposed model, we recommend it for the effective diagnosis of Brain tumors in the E-healthcare system.
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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.