ArticleDiagnostics (Basel, Switzerland)2021
Classification of Brain MRI Tumor Images Based on Deep Learning PGGAN Augmentation.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 71 citations in OpenAlex.
- Neuroimaging and machine learning fusion for improved brain tumor diagnosis and prognosis.Scientific reports · 2026Article
- 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
- Enhancing Radiologist Productivity with Artificial Intelligence in Magnetic Resonance Imaging (MRI): A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- An efficient electricity theft detection based on deep learning.Scientific reports · 2025Article
- Dual-Stream Contrastive Latent Learning Generative Adversarial Network for Brain Image Synthesis and Tumor Classification.Journal of imaging · 2025Article
- Challenges and Advances in Classifying Brain Tumors: An Overview of Machine, Deep Learning, and Hybrid Approaches with Future Perspectives in Medical Imaging.Current medical imaging · 2025Review
- Development of brain tumor radiogenomic classification using GAN-based augmentation of MRI slices in the newly released gazi brains dataset.BMC medical informatics and decision making · 2024Article
- Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology.Cancers · 2024Review
- Breast Ultrasound Images Augmentation and Segmentation Using GAN with Identity Block and Modified U-Net 3.Sensors (Basel, Switzerland) · 2023Article
- Brain Tumor Segmentation Using Deep Learning on MRI Images.Diagnostics (Basel, Switzerland) · 2023Article
- WBM-DLNets: Wrapper-Based Metaheuristic Deep Learning Networks Feature Optimization for Enhancing Brain Tumor Detection.Bioengineering (Basel, Switzerland) · 2023Article
- Attention Deep Feature Extraction from Brain MRIs in Explainable Mode: DGXAINet.Diagnostics (Basel, Switzerland) · 2023Article
- Role of Ensemble Deep Learning for Brain Tumor Classification in Multiple Magnetic Resonance Imaging Sequence Data.Diagnostics (Basel, Switzerland) · 2023Article
- Augmenting healthy brain magnetic resonance images using generative adversarial networks.PeerJ. Computer science · 2023Article
- A Feature Extraction Using Probabilistic Neural Network and BTFSC-Net Model with Deep Learning for Brain Tumor Classification.Journal of imaging · 2022Article
- An Enhanced Machine Learning Approach for Brain MRI Classification.Diagnostics (Basel, Switzerland) · 2022Article
- Convolutional Neural Network Techniques for Brain Tumor Classification (from 2015 to 2022): Review, Challenges, and Future Perspectives.Diagnostics (Basel, Switzerland) · 2022Review
- BrainGAN: Brain MRI Image Generation and Classification Framework Using GAN Architectures and CNN Models.Sensors (Basel, Switzerland) · 2022Article
- Ensemble deep learning for brain tumor detection.Frontiers in computational neuroscience · 2022Article
- MRI Brain Tumor Image Classification Using a Combined Feature and Image-Based Classifier.Frontiers in psychology · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The wide prevalence of brain tumors in all age groups necessitates having the ability to make an early and accurate identification of the tumor type and thus select the most appropriate treatment plans. The application of convolutional neural networks (CNNs) has helped radiologists to more accurately classify the type of brain tumor from magnetic resonance images (MRIs). The learning of CNN suffers from overfitting if a suboptimal number of MRIs are introduced to the system. Recognized as the current best solution to this problem, the augmentation method allows for the optimization of the learning stage and thus maximizes the overall efficiency. The main objective of this study is to examine the efficacy of a new approach to the classification of brain tumor MRIs through the use of a VGG19 features extractor coupled with one of three types of classifiers. A progressive growing generative adversarial network (PGGAN) augmentation model is used to produce 'realistic' MRIs of brain tumors and help overcome the shortage of images needed for deep learning. Results indicated the ability of our framework to classify gliomas, meningiomas, and pituitary tumors more accurately than in previous studies with an accuracy of 98.54%. Other performance metrics were also examined.
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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.