Optimization of Multi-Section and Partially Augmented Magnetic Resonance Imaging (MRI) Images for Brain Tumor Classification Using ResNet-50
DOI:
https://doi.org/10.21512/commit.v19i1.12467Keywords:
Magnetic Resonance Imaging (MRI), Brain Tumor, ResNet-50Abstract
Brain tumor diagnosis is challenging due to complex brain anatomy and tumor variability across imaging views. Traditional methods are manual and error-prone, making deep learning, particularly ResNetbased Convolutional Neural Network (CNN), essential for improving accuracy. The research investigates the enhancement of brain tumor classification using Magnetic Resonance Imaging (MRI) images through a novel modification of the ResNet50 model. It specifically addresses data imbalance challenges in medical image analysis. By proposing a targeted approach to partial data augmentation, the researchers aim to overcome limitations in traditional deep-learning classification methodologies, particularly the performance bottlenecks encountered in differentiating complex brain tumor subtypes. The research uses MRI dataset containing 5,249 labeled images (glioma, meningioma, pituitary, no tumor) across axial, coronal, and sagittal planes, highlighting class and view-based imbalances addressed through targeted augmentation. The research employs transfer learning to analyze three scenarios: non-augmented, partially augmented, and rounding-down data. Results reveal that the partially augmented scenario achieves the highest classification accuracy at 85%, significantly surpassing the non-augmented scenario, which peaks at 79%. In contrast, the rounding-down scenario yields only 60.16% accuracy during validation, highlighting the negative impact of drastically reducing data quantities. The unique contribution lies in demonstrating how strategic partial augmentation can enhance pattern recognition and mitigate overfitting risks, particularly in medical imaging where precise differentiation is crucial. The findings highlight the critical role of nuanced data distribution in enhancing model robustness, as evidenced by improved pattern recognition and reduced overfitting risks in the augmented scenario.
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