ReviewJournal of personalized medicine2022
A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis.
Review in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses 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
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 95 citations in OpenAlex.
- Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews.Journal of medical Internet research · 2025Pooled it
- Federated and Transfer Learning Methods for the Classification of Melanoma and Nonmelanoma Skin Cancers: A Prospective Study.Sensors (Basel, Switzerland) · 2023Pooled it
- Federated learning for privacy-preserving skin cancer classification using deep neural networks.Frontiers in oncology · 2026Article
- Correlation of contrast-enhancing tumor region on pre-radiotherapy MRI with overall survival in glioblastoma.European journal of medical research · 2025Article
- An effective flowchart for multimodal brain tumor binary classification with ranked 3D texture features.Scientific reports · 2025Article
- A federated learning-based privacy-preserving image processing framework for brain tumor detection from CT scans.Scientific reports · 2025Article
- Integrating AI and Assistive Technologies in Healthcare: Insights from a Narrative Review of Reviews.Healthcare (Basel, Switzerland) · 2025Review
- Usage of the National Cancer Institute Cancer Research Data Commons by Researchers: A Scoping Review of the Literature.JCO clinical cancer informatics · 2024Article
- Brain tumor classification utilizing pixel distribution and spatial dependencies higher-order statistical measurements through explainable ML models.Scientific reports · 2024Article
- Emerging research trends in artificial intelligence for cancer diagnostic systems: A comprehensive review.Heliyon · 2024Review
- Multidisciplinary cancer disease classification using adaptive FL in healthcare industry 5.0.Scientific reports · 2024Article
- DVFNet: A deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images.PloS one · 2024Article
- An Explainable MRI-Radiomic Quantum Neural Network to Differentiate Between Large Brain Metastases and High-Grade Glioma Using Quantum Annealing for Feature Selection.Journal of digital imaging · 2023Article
- DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images.Cancers · 2023Article
- Blockchain-Federated and Deep-Learning-Based Ensembling of Capsule Network with Incremental Extreme Learning Machines for Classification of COVID-19 Using CT Scans.Bioengineering (Basel, Switzerland) · 2023Article
- The Role of Machine Learning and Deep Learning Approaches for the Detection of Skin Cancer.Healthcare (Basel, Switzerland) · 2023Article
- DMFL_Net: A Federated Learning-Based Framework for the Classification of COVID-19 from Multiple Chest Diseases Using X-rays.Sensors (Basel, Switzerland) · 2023Article
- SCDNet: A Deep Learning-Based Framework for the Multiclassification of Skin Cancer Using Dermoscopy Images.Sensors (Basel, Switzerland) · 2022Article
- Brain tumor classification using fine-tuned transfer learning models on magnetic resonance imaging (MRI) images.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain tumors are a deadly disease with a high mortality rate. Early diagnosis of brain tumors improves treatment, which results in a better survival rate for patients. Artificial intelligence (AI) has recently emerged as an assistive technology for the early diagnosis of tumors, and AI is the primary focus of researchers in the diagnosis of brain tumors. This study provides an overview of recent research on the diagnosis of brain tumors using federated and deep learning methods. The primary objective is to explore the performance of deep and federated learning methods and evaluate their accuracy in the diagnosis process. A systematic literature review is provided, discussing the open issues and challenges, which are likely to guide future researchers working in the field of brain tumor diagnosis.
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.