A Paradigm Shift in Brain Tumor Classification: Harnessing the Potential of Capsule Networks

Accurate and timely classification of brain tumors is critical for developing effective treatment plans and predicting treatment outcomes. However, CNN-based models commonly used for this task have limitations, such as their reliance on large amounts of training data and difficulties with input orientation and transformations. To address these limitations, we propose a CapsNet-based model for brain tumor classification designed to effectively handle limited datasets, class imbalance, and input transformations. CapsNet relies on “capsules,” groups of neurons that work together to represent specific input image features and are resistant to input orientation and transformations. Our study compares the performance of the proposed CapsNet-based model with state-of-the-art CNN models, and our results demonstrate that the CapsNet-based model outperforms CNN models in terms of accuracy and robustness to input orientation and transformations. These findings suggest that CapsNet has the potential to be a promising alternative to CNNs for accurate and efficient brain tumor classification.