ArticleScientific reports2025
Automated multi-class MRI brain tumor classification and segmentation using deformable attention and saliency mapping.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- A novel hybrid transformer-based framework (H-ConvNeXt-Swin) to classify brain tumors using MRI.Scientific reports · 2026Article
- Multi-class classification of brain tumor using a ResNet101 backbone integrated with multi-scale deformable attention module and advanced data augmentations.Scientific reports · 2026Article
- A Robust ConvNeXt-Based Framework for Efficient, Generalizable, and Explainable Brain Tumor Classification on MRI.Bioengineering (Basel, Switzerland) · 2026Article
- Hybrid 2D/3D CNN and radiomics model for brain tumor classification using EfficientNetb0 and ResNet-18.BMC medical imaging · 2026Article
- Query-guided learning for efficient and interpretable multi-class brain tumor classification in MRI.Frontiers in artificial intelligence · 2026Article
- Evolving optimal text clusters: A novel GA-driven framework for dynamic ensemble fusion of multi-model contextual embeddings.PloS one · 2026Article
- Investigating discrepancies in accuracy, agreement and interpretability for single-frame embryo classification tasks conducted by embryologists and deep learning models.Frontiers in reproductive health · 2026Article
- A novel hybrid framework integrating GA-driven 3D ResUNetGAN for MRI brain tumor segmentation.PloS one · 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
- D-S-Net: an efficient dual-stage strategy for high-precision segmentation of gross tumor volumes in lung cancer CT images.BMC cancer · 2025Article
- Multiple deep learning models based on MRI images in discriminating glioblastoma from solitary brain metastases: a multicentre study.BMC medical imaging · 2025Article
- MAUNet: a mixed attention U-net with spatial multi-dimensional convolution and contextual feature calibration for 3D brain tumor segmentation in multimodal MRI.Frontiers in neuroscience · 2025Article
- DPEA-Net: a clinically-oriented lightweight 3D CNN for glioma segmentation in multiparametric MRI.Frontiers in neurologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the diagnosis and treatment of brain tumors, the automatic classification and segmentation of medical images play a pivotal role. Early detection facilitates timely intervention, significantly improving patient survival rates. This study introduces a novel method for the automated classification and segmentation of brain tumors, aiming to enhance both diagnostic accuracy and efficiency. Magnetic Resonance (MR) imaging remains the gold standard in clinical brain tumor diagnostics; however, it is a time-intensive and labor-intensive process. Consequently, the integration of automated detection, localization, and classification methods is not only desirable but essential. In this research, we present a novel framework that enables both tumor classification and post-classification diagnostic feature extraction, allowing for the first-time classification of multiple tumor types. To improve tumor characterization, we applied data augmentation techniques to MR images and developed a hierarchical multiscale deformable attention module (MS-DAM). This model effectively captures irregular and complex tumor patterns, enhancing classification performance. Following classification, a comprehensive segmentation process was conducted across a large dataset, reinforcing the model's role as a decision support system. Utilizing a Kaggle dataset containing 14 different tumor types with highly similar morphologic structures, we validated the proposed model's efficacy. Compared to existing multi-scale channel attention modules, MS-DAM achieved superior accuracy, exceeding 96.5%. This study presents a highly promising approach for the automated classification and segmentation of brain tumors in medical imaging, offering significant advancements for diagnostic imaging clinics and paving the way for more efficient, accurate, and scalable tumor detection methodologies.
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.