ArticleJournal of digital imaging2020
Deep Multi-Scale 3D Convolutional Neural Network (CNN) for MRI Gliomas Brain Tumor Classification.
Article in Journal of digital imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers, 2 of them syntheses that pooled it.
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Who cites it
77 citing papers in PubMed, 2 syntheses or guidelines pooled it, 344 citations in OpenAlex.
- Noninvasive grading of glioma brain tumors using magnetic resonance imaging and deep learning methods.Journal of cancer research and clinical oncology · 2023Pooled it
- Deep learning algorithm performance in contouring head and neck organs at risk: a systematic review and single-arm meta-analysis.Biomedical engineering online · 2023Pooled it
- An Adaptive Multi-Stage and Adjacent-Level Feature Integration Network for Brain Tumor Image Segmentation.Interdisciplinary sciences, computational life sciences · 2026Article
- MANet: a multimodal attention convolutional neural network for brain tumor classification.Scientific reports · 2026Article
- Role of Three-Dimensional Convolution Neural Networks (3D-CNN) in Image Processing and Recognition in Oncology: A Systematic Review and Meta-Analysis.International journal of hematology-oncology and stem cell research · 2026Review
- Rectal Cancer Radiotherapy Response Prediction: Retrospective Study of Development of a Deep Learning-Based Radiomics Model.JMIR medical informatics · 2026Article
- Adaptive multi-feature fusion architecture with optimized learning for high-fidelity brain tumor classification in MRI.Scientific reports · 2026Article
- Challenges in Using Deep Neural Networks Across Multiple Readers in Delineating Prostate Gland Anatomy.Journal of imaging informatics in medicine · 2026Article
- ASLNet: an explainable deep learning framework for glioma grading and survival prediction.Frontiers in oncology · 2026Article
- Development and validation of explainable deep learning models for classification of atrial fibrillation subtypes using cardiac computed tomography.Heart rhythm O2 · 2025Article
- Development of a Fully Optimized Convolutional Neural Network for Astrocytoma Classification in MRI Using Explainable Artificial Intelligence.Journal of imaging · 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
- Generating Synthetic MR Spectroscopic Imaging Data with Generative Adversarial Networks to Train Machine Learning Models.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Article
- Deep learning approaches for classification tasks in medical X-ray, MRI, and ultrasound images: a scoping review.BMC medical imaging · 2025Article
- A Methodological Framework for AI-Assisted Diagnosis of Ovarian Masses Using CT and MR Imaging.Journal of personalized medicine · 2025Article
- Review
- A hybrid explainable model based on advanced machine learning and deep learning models for classifying brain tumors using MRI images.Scientific reports · 2025Article
- Article
- Brain CT image classification based on mask RCNN and attention mechanism.Scientific reports · 2024Article
- End-to-End Multi-task Learning Architecture for Brain Tumor Analysis with Uncertainty Estimation in MRI Images.Journal of imaging informatics in medicine · 2024Article
17 more citing papers are in PubMed but not listed here.
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 at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate and fully automatic brain tumor grading from volumetric 3D magnetic resonance imaging (MRI) is an essential procedure in the field of medical imaging analysis for full assistance of neuroradiology during clinical diagnosis. We propose, in this paper, an efficient and fully automatic deep multi-scale three-dimensional convolutional neural network (3D CNN) architecture for glioma brain tumor classification into low-grade gliomas (LGG) and high-grade gliomas (HGG) using the whole volumetric T1-Gado MRI sequence. Based on a 3D convolutional layer and a deep network, via small kernels, the proposed architecture has the potential to merge both the local and global contextual information with reduced weights. To overcome the data heterogeneity, we proposed a preprocessing technique based on intensity normalization and adaptive contrast enhancement of MRI data. Furthermore, for an effective training of such a deep 3D network, we used a data augmentation technique. The paper studied the impact of the proposed preprocessing and data augmentation on classification accuracy.Quantitative evaluations, over the well-known benchmark (Brats-2018), attest that the proposed architecture generates the most discriminative feature map to distinguish between LG and HG gliomas compared with 2D CNN variant. The proposed approach offers promising results outperforming the recently supervised and unsupervised state-of-the-art approaches by achieving an overall accuracy of 96.49% using the validation dataset. The obtained experimental results confirm that adequate MRI's preprocessing and data augmentation could lead to an accurate classification when exploiting CNN-based approaches.
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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.