ArticleCancers2023
Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers, 2 of them syntheses that pooled it.
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Who cites it
90 citing papers in PubMed, 2 syntheses or guidelines pooled it, 383 citations in OpenAlex.
- Deep Learning Approaches for Brain Tumor Detection and Classification Using MRI Images (2020 to 2024): A Systematic Review.Journal of imaging informatics in medicine · 2025Pooled it
- Neuroimage analysis using artificial intelligence approaches: a systematic review.Medical & biological engineering & computing · 2024Pooled it
- SERA-Net: Rethinking CNN Design for Brain Tumor Classification via Squeeze-and-Excite Attention and Residual Learning.Bioengineering (Basel, Switzerland) · 2026Article
- Brain Tumor Segmentation and Grading on MRI Using Deep Learning: A Systematic Literature Review and Benchmark-Driven Comparative Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- Brain Tumor Classification Using Convolutional Neural Network and Bitterling Fish Optimization Algorithm.Diagnostics (Basel, Switzerland) · 2026Article
- Research progress of deep learning based on magnetic resonance imaging in meningioma.Magma (New York, N.Y.) · 2026Review
- A Class-Wise Deep Ensemble Framework Using ResNet101 and DenseNet201 for Brain Tumor Classification.Journal of imaging informatics in medicine · 2026Article
- CAE-BrainNet: a statistically validated class-adaptive attention ensemble model for explainable brain tumor classification from MRI.Brain informatics · 2026Article
- Challenges and Vision for Standardization of Biopolymer Data Sets for Machine Learning.Biomacromolecules · 2026Review
- 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
- ETDACVO: Structural-Fidelity-Aware Evolutionary Co-Optimization for Robust and Explainable Brain Tumor MRI Classification.Biomedicines · 2026Article
- 3D Deep Learning for Brain Tumor Segmentation and Survival Prediction: A Comprehensive Multi-Modal Analysis Using the BraTS2020 Dataset.Journal of imaging · 2026Article
- Brain tumor classification using fractional Laplacian image enhancement and vision transformer.Scientific reports · 2026Article
- YOLO-LS: a novel deep learning framework for brain tumor segmentation in Magnetic Resonance Imaging.Scientific reports · 2026Article
- Artificial Intelligence-Based MRI Segmentation in Glioblastoma and Single Brain Metastasis: An Exploratory Study of Diagnostic and Prognostic Value.Life (Basel, Switzerland) · 2026Article
- Detection of Interpretable and Fine-Grained Brain Tumor Magnetic Resonance Imaging Based on Progressive Pruning: Machine Learning Model Development and Validation Study.JMIR medical informatics · 2026Article
- An advanced hybrid deep learning framework for high-precision brain tumor detection and classification in MRI scans.Scientific reports · 2026Article
- Neuroimaging and machine learning fusion for improved brain tumor diagnosis and prognosis.Scientific reports · 2026Article
- Multimodal deep feature fusion with transformer for brain tumor classification from magnetic resonance imaging.Scientific reports · 2026Article
- MRI-based brain tumor prediction using convolutional neural network framework.Scientific reports · 2026Article
30 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid development of abnormal brain cells that characterizes a brain tumor is a major health risk for adults since it can cause severe impairment of organ function and even death. These tumors come in a wide variety of sizes, textures, and locations. When trying to locate cancerous tumors, magnetic resonance imaging (MRI) is a crucial tool. However, detecting brain tumors manually is a difficult and time-consuming activity that might lead to inaccuracies. In order to solve this, we provide a refined You Only Look Once version 7 (YOLOv7) model for the accurate detection of meningioma, glioma, and pituitary gland tumors within an improved detection of brain tumors system. The visual representation of the MRI scans is enhanced by the use of image enhancement methods that apply different filters to the original pictures. To further improve the training of our proposed model, we apply data augmentation techniques to the openly accessible brain tumor dataset. The curated data include a wide variety of cases, such as 2548 images of gliomas, 2658 images of pituitary, 2582 images of meningioma, and 2500 images of non-tumors. We included the Convolutional Block Attention Module (CBAM) attention mechanism into YOLOv7 to further enhance its feature extraction capabilities, allowing for better emphasis on salient regions linked with brain malignancies. To further improve the model's sensitivity, we have added a Spatial Pyramid Pooling Fast+ (SPPF+) layer to the network's core infrastructure. YOLOv7 now includes decoupled heads, which allow it to efficiently glean useful insights from a wide variety of data. In addition, a Bi-directional Feature Pyramid Network (BiFPN) is used to speed up multi-scale feature fusion and to better collect features associated with tumors. The outcomes verify the efficiency of our suggested method, which achieves a higher overall accuracy in tumor detection than previous state-of-the-art models. As a result, this framework has a lot of potential as a helpful decision-making tool for experts in the field of diagnosing brain tumors.
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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.