ArticleSensors (Basel, Switzerland)2022
BrainGAN: Brain MRI Image Generation and Classification Framework Using GAN Architectures and CNN Models.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep Learning Approaches for Brain Tumor Detection and Classification Using MRI Images (2020 to 2024): A Systematic Review.Journal of imaging informatics in medicine · 2025Pooled it
- Application of generative artificial intelligence (AI) to support pain neuroscience research in persons who are pregnant.Communications medicine · 2026Article
- Realistic PET image synthesis from MRI for automated inference of brain atrophy and Alzheimer's.iScience · 2026Article
- RAUM-GANs: a multi-layer GAN-enhanced framework for accurate multiple sclerosis lesion segmentation in MRI.Scientific reports · 2025Article
- Laplacian filter attention with style transfer GAN for brain tumor MRI imputation.Scientific reports · 2025Article
- Investigating brain tumor classification using MRI: a scientometric analysis of selected articles from 2015 to 2024.Neuroradiology · 2025Review
- A novel deep neural architecture for efficient and scalable multidomain image classification.Scientific reports · 2025Article
- SECONDGRAM: Self-conditioned diffusion with gradient manipulation for longitudinal MRI imputation.Patterns (New York, N.Y.) · 2025Article
- MRI2PET: Realistic PET Image Synthesis from MRI for Automated Inference of Brain Atrophy and Alzheimer's.medRxiv : the preprint server for health sciences · 2025Article
- 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
- Generative artificial intelligence to produce high-fidelity blastocyst-stage embryo images.Human reproduction (Oxford, England) · 2024Article
- A Deep Learning Based Intelligent Decision Support System for Automatic Detection of Brain Tumor.Biomedical engineering and computational biology · 2024Article
- Enhancing Fetal Anomaly Detection in Ultrasonography Images: A Review of Machine Learning-Based Approaches.Biomimetics (Basel, Switzerland) · 2023Review
- Detection of the rotator cuff tears using a novel convolutional neural network from magnetic resonance image (MRI).Heliyon · 2023Article
- Brain Tumor Segmentation Using Deep Learning on MRI Images.Diagnostics (Basel, Switzerland) · 2023Article
- A Multi-Purpose Shallow Convolutional Neural Network for Chart Images.Sensors (Basel, Switzerland) · 2022Article
- A Robust End-to-End Deep Learning-Based Approach for Effective and Reliable BTD Using MR Images.Sensors (Basel, Switzerland) · 2022Article
- Hypertension Diagnosis with Backpropagation Neural Networks for Sustainability in Public Health.Sensors (Basel, Switzerland) · 2022Article
- Semi-ProtoPNet Deep Neural Network for the Classification of Defective Power Grid Distribution Structures.Sensors (Basel, Switzerland) · 2022Article
- Ensemble deep learning for brain tumor detection.Frontiers in computational neuroscience · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning models have been used in several domains, however, adjusting is still required to be applied in sensitive areas such as medical imaging. As the use of technology in the medical domain is needed because of the time limit, the level of accuracy assures trustworthiness. Because of privacy concerns, machine learning applications in the medical field are unable to use medical data. For example, the lack of brain MRI images makes it difficult to classify brain tumors using image-based classification. The solution to this challenge was achieved through the application of Generative Adversarial Network (GAN)-based augmentation techniques. Deep Convolutional GAN (DCGAN) and Vanilla GAN are two examples of GAN architectures used for image generation. In this paper, a framework, denoted as BrainGAN, for generating and classifying brain MRI images using GAN architectures and deep learning models was proposed. Consequently, this study proposed an automatic way to check that generated images are satisfactory. It uses three models: CNN, MobileNetV2, and ResNet152V2. Training the deep transfer models with images made by Vanilla GAN and DCGAN, and then evaluating their performance on a test set composed of real brain MRI images. From the results of the experiment, it was found that the ResNet152V2 model outperformed the other two models. The ResNet152V2 achieved 99.09% accuracy, 99.12% precision, 99.08% recall, 99.51% area under the curve (AUC), and 0.196 loss based on the brain MRI images generated by DCGAN architecture.
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