ReviewSensors (Basel, Switzerland)2025
Advanced Deep Learning and Machine Learning Techniques for MRI Brain Tumor Analysis: A Review.
Review in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Brain Tumor Segmentation and Grading on MRI Using Deep Learning: A Systematic Literature Review and Benchmark-Driven Comparative Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- Exploring the role of advanced MRI in understanding glioblastoma biology: A scoping review protocol.MethodsX · 2026Article
- Exploring the Role of Advanced MRI in Understanding Glioblastoma Biology: A Scoping Review.Cancers · 2026Review
- A New Encoding Architecture Based on Shift Multilayer Perceptron and Transformer for Medical Image Segmentation.Sensors (Basel, Switzerland) · 2026Article
- Lightweight deep learning for medical imaging using MobileNetV2-based brain pathology classification with Grad-CAM interpretability.Frontiers in medicine · 2026Article
- Multi-class brain tumor MRI segmentation and classification using deep learning and machine learning approaches.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Brain tumor segmentation using deep learning: high performance with minimized MRI data.Frontiers in radiology · 2025Article
- Lightweight CNN for accurate brain tumor detection from MRI with limited training data.Frontiers in medicine · 2025Article
- Explainable AI-driven MRI-based brain tumor classification: a novel deep learning approach.Frontiers in artificial intelligence · 2025Article
- Convolutional neural networks: applications, challenges and future prospects in brain tumor research.Frontiers in neurologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
A brain tumor is the result of abnormal growth of cells in the central nervous system (CNS), widely considered as a complex and diverse clinical entity that is difficult to diagnose and cure. In this study, we focus on current advances in medical imaging, particularly magnetic resonance imaging (MRI), and how machine learning (ML) and deep learning (DL) algorithms might be combined with clinical assessments to improve brain tumor diagnosis. Due to its superior contrast resolution and safety compared to other imaging methods, MRI is highlighted as the preferred imaging modality for brain tumors. The challenges related to brain tumor analysis in different processes including detection, segmentation, classification, and survival prediction are addressed along with how ML/DL approaches significantly improve these steps. We systematically analyzed 107 studies (2018-2024) employing ML, DL, and hybrid models across publicly available datasets such as BraTS, TCIA, and Figshare. In the light of recent developments in brain tumor analysis, many algorithms have been proposed to accurately obtain ontological characteristics of tumors, enhancing diagnostic precision and personalized therapeutic strategies.
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.