CNN-Based Image Segmentation Approach in Brain Tumor Classification: A Review
Nurul Huda, Ku Ruhana Ku‐Mahamud
Abstract
This study explores the application of Convolutional Neural Networks (CNNs) for brain tumor segmentation, leveraging their ability to automatically extract hierarchical features from medical images. CNN architectures like U-Net, V-Net, and ResNet have shown significant promise in brain tumor classification, offering high precision in detecting tumor boundaries and classifying tumor types. Various benchmark datasets, such as BraTS, TCIA, Harvard, and Kaggle, provide annotated MRI images to evaluate these models. Performance metrics including Dice Similarity Coefficient (DSC), Intersection over Union, and accuracy are employed to assess the models’ effectiveness. The results demonstrate that CNN-based models, particularly U-Net, perform exceptionally well, with DSC scores exceeding 0.90 in most cases. However, challenges such as data imbalance, the need for large datasets, and high computational demands persist. Despite these limitations, CNNs, when combined with advanced techniques like transfer learning and data augmentation, offer robust solutions for brain tumor segmentation, showing promise for real-time clinical deployment. Further advancements are necessary to address generalization issues and enhance model efficiency, ensuring broader applicability in clinical settings.