ArticleDiagnostics (Basel, Switzerland)2021
A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 158 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
- W-AGRU-Net: a dual-stream attention framework for robust brain tumor segmentation in clinical MRI imaging.Scientific reports · 2026Article
- A denoising autoencoder-enhanced ResNet 50 framework for explainable brain tumor classification using MRI.Frontiers in oncology · 2026Article
- Enhanced temporal encoding-decoding for survival analysis of multimodal clinical data in smart healthcare.Visual computing for industry, biomedicine, and art · 2025Article
- Brain Tumour Classification Model Based on Spatial Block-Residual Block Collaborative Architecture with Strip Pooling Feature Fusion.Journal of imaging · 2025Article
- Development and validation of a keypoint region-based convolutional neural network to automate thoracic Cobb angle measurements using whole-spine standing radiographs.Acta neurochirurgica · 2025Article
- Monochromatic LeafAdaptNet (MLAN): an adaptive approach to spinach leaf disease detection using monochromatic imaging.World journal of microbiology & biotechnology · 2025Article
- A Hybrid Model of Feature Extraction and Dimensionality Reduction Using ViT, PCA, and Random Forest for Multi-Classification of Brain Cancer.Diagnostics (Basel, Switzerland) · 2025Article
- Article
- Challenges and Advances in Classifying Brain Tumors: An Overview of Machine, Deep Learning, and Hybrid Approaches with Future Perspectives in Medical Imaging.Current medical imaging · 2025Review
- IV3TM: Inception V3 enabled bidirectional long short-term memory network for brain tumor classification.PloS one · 2025Article
- Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology.Cancers · 2024Review
- ContourTL-Net: Contour-Based Transfer Learning Algorithm for Early-Stage Brain Tumor Detection.International journal of biomedical imaging · 2024Article
- Trade-off between training and testing ratio in machine learning for medical image processing.PeerJ. Computer science · 2024Article
- A Circular Box-Based Deep Learning Model for the Identification of Signet Ring Cells from Histopathological Images.Bioengineering (Basel, Switzerland) · 2023Article
- Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging.Cancers · 2023Article
- Automated Detection and Diagnosis of Spinal Schwannomas and Meningiomas Using Deep Learning and Magnetic Resonance Imaging.Journal of clinical medicine · 2023Article
- A Novel Approach for Brain Tumor Classification Using an Ensemble of Deep and Hand-Crafted Features.Sensors (Basel, Switzerland) · 2023Article
- Brain tumour detection from magnetic resonance imaging using convolutional neural networks.Contemporary oncology (Poznan, Poland) · 2023Article
- DCNet: DenseNet-77-based CornerNet model for the tomato plant leaf disease detection and classification.Frontiers in plant science · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 5 institutions in 3 countries.
Funding
Abstract
A brain tumor is an abnormal growth in brain cells that causes damage to various blood vessels and nerves in the human body. An earlier and accurate diagnosis of the brain tumor is of foremost important to avoid future complications. Precise segmentation of brain tumors provides a basis for surgical planning and treatment to doctors. Manual detection using MRI images is computationally complex in cases where the survival of the patient is dependent on timely treatment, and the performance relies on domain expertise. Therefore, computerized detection of tumors is still a challenging task due to significant variations in their location and structure, i.e., irregular shapes and ambiguous boundaries. In this study, we propose a custom Mask Region-based Convolution neural network (Mask RCNN) with a densenet-41 backbone architecture that is trained via transfer learning for precise classification and segmentation of brain tumors. Our method is evaluated on two different benchmark datasets using various quantitative measures. Comparative results show that the custom Mask-RCNN can more precisely detect tumor locations using bounding boxes and return segmentation masks to provide exact tumor regions. Our proposed model achieved an accuracy of 96.3% and 98.34% for segmentation and classification respectively, demonstrating enhanced robustness compared to state-of-the-art approaches.
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.