ArticleBrain communications2024
Enhanced MRI-based brain tumour classification with a novel Pix2pix generative adversarial network augmentation framework.
Article in Brain communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- A Systematic Review on Synthetic Medical Images Generation-Recent Trends and Future Opportunities.Diagnostics (Basel, Switzerland) · 2026Review
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- Advanced Deep Learning Models for Classifying Dental Diseases from Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- Development of an Intelligent Clinical Decision Support System for Spirometry Quality Control.Diagnostics (Basel, Switzerland) · 2026Article
- Deep Learning for Brain Tumour Analysis: A Systematic Review of CNN-Transformer Hybrids in Multimodal Imaging.International journal of biomedical imaging · 2026Review
- Longitudinal Cerebral Structural, Microstructural, and Functional Alterations After Brain Tumor Surgery for Early Detection of Recurrent Tumors.Biomedicines · 2025Article
- Radiomics in Action: Multimodal Synergies for Imaging Biomarkers.Bioengineering (Basel, Switzerland) · 2025Review
- AI-Driven Innovations in Neuroradiology and Neurosurgery: Scoping Review of Current Evidence and Future Directions.Cancers · 2025Review
- Droplet Digital PCR Improves Detection ofInternational journal of molecular sciences · 2025Article
- Detection ofJournal of imaging · 2025Article
- Federated learning-based CT liver tumor detection using a teacher‒student SANet with semisupervised learning.BMC medical imaging · 2025Article
- Imaging Evaluation of Periarticular Soft Tissue Masses in the Appendicular Skeleton: A Pictorial Review.Journal of imaging · 2025Review
- Advancements in Radiology Report Generation: A Comprehensive Analysis.Bioengineering (Basel, Switzerland) · 2025Review
- Variable Selection for Multivariate Failure Time Data via Regularized Sparse-Input Neural Network.Bioengineering (Basel, Switzerland) · 2025Article
- AI-Driven Automated Blood Cell Anomaly Detection: Enhancing Diagnostics and Telehealth in Hematology.Journal of imaging · 2025Article
- AI-Powered Object Detection in Radiology: Current Models, Challenges, and Future Direction.Journal of imaging · 2025Review
- AI in 2D Mammography: Improving Breast Cancer Screening Accuracy.Medicina (Kaunas, Lithuania) · 2025Article
- DermViT: Diagnosis-Guided Vision Transformer for Robust and Efficient Skin Lesion Classification.Bioengineering (Basel, Switzerland) · 2025Article
- Automatic Analysis of Ultrasound Images to Estimate Subcutaneous and Visceral Fat and Muscle Tissue in Patients with Suspected Malnutrition.Diagnostics (Basel, Switzerland) · 2025Article
Corrections and comments
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4 authors.
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Abstract
The scarcity of medical imaging datasets and privacy concerns pose significant challenges in artificial intelligence-based disease prediction. This poses major concerns to patient confidentiality as there are now tools capable of extracting patient information by merely analysing patient's imaging data. To address this, we propose the use of synthetic data generated by generative adversarial networks as a solution. Our study pioneers the utilisation of a novel Pix2Pix generative adversarial network model, specifically the 'image-to-image translation with conditional adversarial networks,' to generate synthetic datasets for brain tumour classification. We focus on classifying four tumour types: glioma, meningioma, pituitary and healthy. We introduce a novel conditional deep convolutional neural network architecture, developed from convolutional neural network architectures, to process the pre-processed generated synthetic datasets and the original datasets obtained from the Kaggle repository. Our evaluation metrics demonstrate the conditional deep convolutional neural network model's high performance with synthetic images, achieving an accuracy of 86%. Comparative analysis with state-of-the-art models such as Residual Network50, Visual Geometry Group 16, Visual Geometry Group 19 and InceptionV3 highlights the superior performance of our conditional deep convolutional neural network model in brain tumour detection, diagnosis and classification. Our findings underscore the efficacy of our novel Pix2Pix generative adversarial network augmentation technique in creating synthetic datasets for accurate brain tumour classification, offering a promising avenue for improved disease prediction and treatment planning.
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