ArticleProceedings of SPIE--the International Society for Optical Engineering2017
Deep Learning for Brain Tumor Classification.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- BrainView: A cloud-based deep learning system for brain image segmentation, tumor detection and visualization.Biomedical journal · 2026Article
- Brain tumor classification from MRI images using a multi-scale channel attention CNN integrated with SVM.Scientific reports · 2026Article
- Synergizing advanced algorithm of explainable artificial intelligence with hybrid model for enhanced brain tumor detection in healthcare.Scientific reports · 2025Article
- Hybrid model integration with explainable AI for brain tumor diagnosis: a unified approach to MRI analysis and prediction.Scientific reports · 2025Article
- Optimizing Tumor Detection in Brain MRI with One-Class SVM and Convolutional Neural Network-Based Feature Extraction.Journal of imaging · 2025Article
- A Hybrid Model of Feature Extraction and Dimensionality Reduction Using ViT, PCA, and Random Forest for Multi-Classification of Brain Cancer.Diagnostics (Basel, Switzerland) · 2025Article
- An automated deep learning framework for brain tumor classification using MRI imagery.Scientific reports · 2025Article
- Explainable CNN for brain tumor detection and classification through XAI based key features identification.Brain informatics · 2025Article
- A novel approach for the detection of brain tumor and its classification via independent component analysis.Scientific reports · 2025Article
- T1-weighted MRI-based brain tumor classification using hybrid deep learning models.Scientific reports · 2025Article
- A Hybrid Deep Learning Model with Data Augmentation to Improve Tumor Classification Using MRI Images.Diagnostics (Basel, Switzerland) · 2024Article
- Advancing brain tumor classification through MTAP model: an innovative approach in medical diagnostics.Medical & biological engineering & computing · 2024Article
- Brain Tumor MRI Classification Using a Novel Deep Residual and Regional CNN.Biomedicines · 2024Article
- Brain tumor detection from images and comparison with transfer learning methods and 3-layer CNN.Scientific reports · 2024Article
- Deep learning in precision medicine and focus on glioma.Bioengineering & translational medicine · 2023Review
- An Ensemble Model for the Diagnosis of Brain Tumors through MRIs.Diagnostics (Basel, Switzerland) · 2023Article
- Role of Ensemble Deep Learning for Brain Tumor Classification in Multiple Magnetic Resonance Imaging Sequence Data.Diagnostics (Basel, Switzerland) · 2023Article
- Deep Learning-Based Computer-Aided Diagnosis (CAD): Applications for Medical Image Datasets.Sensors (Basel, Switzerland) · 2022Article
- Parallelistic Convolution Neural Network Approach for Brain Tumor Diagnosis.Diagnostics (Basel, Switzerland) · 2022Article
- Isolated Convolutional-Neural-Network-Based Deep-Feature Extraction for Brain Tumor Classification Using Shallow Classifier.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Recent research has shown that deep learning methods have performed well on supervised machine learning, image classification tasks. The purpose of this study is to apply deep learning methods to classify brain images with different tumor types: meningioma, glioma, and pituitary. A dataset was publicly released containing 3,064 T1-weighted contrast enhanced MRI (CE-MRI) brain images from 233 patients with either meningioma, glioma, or pituitary tumors split across axial, coronal, or sagittal planes. This research focuses on the 989 axial images from 191 patients in order to avoid confusing the neural networks with three different planes containing the same diagnosis. Two types of neural networks were used in classification: fully connected and convolutional neural networks. Within these two categories, further tests were computed via the augmentation of the original 512×512 axial images. Training neural networks over the axial data has proven to be accurate in its classifications with an average five-fold cross validation of 91.43% on the best trained neural network. This result demonstrates that a more general method (i.e. deep learning) can outperform specialized methods that require image dilation and ring-forming subregions on tumors.
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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.