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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
Published on: January 7, 2019
A deep learning based NeuroFusionNet approach for automated brain tumor diagnosis from MRI
Omara Mustafa1, Salem Alhatamleh2, Hamad Yahia Abu Mhanna3
1Department of Radiology, Korean Medical Center, Lusail, Qatar.
Background:
Brain tumor diagnosis from magnetic resonance imaging (MRI) remains a challenging task due to the high variability in tumor appearance and the limitations of manual interpretation.
Methods:
To address these challenges, this paper proposes NeuroFusionNet, a deep learning framework for automated brain tumor classification from MRI. The framework integrates GAN-based synthetic image generation with transfer learning using a fine-tuned VGG16 backbone. Real and GAN-generated MRI images are passed through VGG16 to extract discriminative feature representations, which are then used for final classification. To adapt the model to domain-specific MRI characteristics while preserving pretrained knowledge, the last ten layers of VGG16 are fine-tuned and the remaining layers are kept frozen.
Results:
The effectiveness of NeuroFusionNet is validated on two publicly available brain MRI datasets. Experimental results demonstrate that the proposed learning framework achieves classification accuracies of 99.05 and 98.75% on the Brain Tumor MRI Dataset and the MRI with Bounding Boxes Dataset, respectively, consistently outperforming several state-of-the-art neural architectures, including VGG16, VGG19, MobileNetV2, DenseNet121, and NASNetLarge.
Conclusion:
The results suggest that NeuroFusionNet is effective for the evaluated public MRI datasets; additional external validation is required.
