ReviewBrain sciences2020
Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges.
Review in Brain sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
48 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of artificial intelligence and radiomics in pituitary neuroendocrine and sellar tumors: a quantitative and qualitative synthesis.Neuroradiology · 2022Pooled it
- A class-wise quantum relational calibration network for brain tumor diagnosis.Scientific reports · 2026Article
- Optimised Deep Learning for Gastrointestinal Polyp Classification: A Controlled Benchmark of Five CNN and Transformer Architectures with Grad-CAM Interpretability.Diagnostics (Basel, Switzerland) · 2026Article
- A Machine learning pipeline to investigate tissue ingrowth in cerebral aneurysms using preclinical animal models.Scientific reports · 2026Article
- Research hotspots and trends of pediatric bone age: A bibliometric and visualization analysis.Lasers in medical science · 2025Article
- Multi-Class Brain Tumor Grades Classification Using a Deep Learning-Based Majority Voting Algorithm and Its Validation Using Explainable-AI.Journal of imaging informatics in medicine · 2025Article
- Prediction of 1p/19q state in glioma by integrated deep learning method based on MRI radiomics.BMC cancer · 2025Article
- Review
- Transfer learning for accurate brain tumor classification in MRI: a step forward in medical diagnostics.Discover oncology · 2025Article
- Explainable CNN for brain tumor detection and classification through XAI based key features identification.Brain informatics · 2025Article
- Context aware machine learning techniques for brain tumor classification and detection - A review.Heliyon · 2025Review
- Advanced Brain Tumor Classification in MR Images Using Transfer Learning and Pre-Trained Deep CNN Models.Cancers · 2025Article
- ESA-YOLOv5m: a lightweight spatial and improved attention-driven detection for brain tumor MRI analysis.Frontiers in medicine · 2025Article
- Machine learning identification of molecular targets for medulloblastoma subgroups using microarray gene fingerprint analysis.Computational and structural biotechnology journal · 2025Article
- The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection.Journal of imaging · 2024Review
- Differential diagnosis of congenital ventricular septal defect and atrial septal defect in children using deep learning-based analysis of chest radiographs.BMC pediatrics · 2024Article
- Deep learning automatic semantic segmentation of glioblastoma multiforme regions on multimodal magnetic resonance images.International journal of computer assisted radiology and surgery · 2024Article
- Multidisciplinary cancer disease classification using adaptive FL in healthcare industry 5.0.Scientific reports · 2024Article
- Enhancing Automated Brain Tumor Detection Accuracy Using Artificial Intelligence Approaches for Healthcare Environments.Bioengineering (Basel, Switzerland) · 2024Article
- Multimodal Brain Tumor Classification Using Convolutional Tumnet Architecture.Behavioural neurology · 2024Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep Learning (DL) algorithms enabled computational models consist of multiple processing layers that represent data with multiple levels of abstraction. In recent years, usage of deep learning is rapidly proliferating in almost every domain, especially in medical image processing, medical image analysis, and bioinformatics. Consequently, deep learning has dramatically changed and improved the means of recognition, prediction, and diagnosis effectively in numerous areas of healthcare such as pathology, brain tumor, lung cancer, abdomen, cardiac, and retina. Considering the wide range of applications of deep learning, the objective of this article is to review major deep learning concepts pertinent to brain tumor analysis (e.g., segmentation, classification, prediction, evaluation.). A review conducted by summarizing a large number of scientific contributions to the field (i.e., deep learning in brain tumor analysis) is presented in this study. A coherent taxonomy of research landscape from the literature has also been mapped, and the major aspects of this emerging field have been discussed and analyzed. A critical discussion section to show the limitations of deep learning techniques has been included at the end to elaborate open research challenges and directions for future work in this emergent area.
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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.