Akademik Veri Yönetim Sistemi
CLUSTER COMPUTING, cilt.29, sa.148, ss.1-29, 2026 (SCI-Expanded, Scopus)
Accurate and early detection of brain anomalies using MRI is critical
for effective diagnosis and treatment planning.
Recent advances in deep learning, particularly convolutional neural
networks, have significantly improved the capabili-
ties of automated medical image analysis, enabling more precise and
scalable solutions in clinical settings. However, the
performance of such models is highly dependent on the quality,
diversity, and annotation detail of the training datasets.
Building on this foundation, in this study, we investigate the
performance of augmented deep neural networks for MRI-
based brain anomaly classification using the BraTS 2021 dataset and our
newly introduced Gazi Brains 2025 dataset,
which includes MRI scans from 500 patients, a substantial portion of
which are annotated for various brain anomalies.
The dataset supports both binary (tumor versus normal) and multiclass
(seven neurological conditions) classification
tasks. We develop and evaluate a series of deep learning and
transformer-based models for anomaly classification, with
a particular focus on the impact of synthetic data augmentation. Using
StyleGANv3 and Guided Diffusion, we gener-
ate synthetic MRI scans to enhance training data diversity and examine
their effect on model performance. In addition,
a ß-VAE–based generative pipeline is employed to further expand the
synthetic dataset, providing controllable latent
representations and additional variability through VAE sampling.
Experimental results show that all three augmentation
strategies-StyleGANv3, Guided Diffusion, and ß-VAE- significantly
improve classification accuracy compared to baseline
models trained solely on the original dataset. DenseNet achieved an
accuracy value of up to 91% in binary classification
when trained with augmented data, and EfficientNetV2S also achieved up
to 72% in multiclass classification. Although
StyleGANv3-generated images exhibited superior visual quality (low FID
scores), the method was limited in sample
volume. In contrast, the diffusion-based approach allowed the creation
of larger synthetic datasets, though at the cost of
extended training and sampling times. The ß-VAE model produced a
moderate number of anatomically coherent samples
with lower computational cost, offering a balanced alternative between
quality and scalability.