One potential limitation of the VMamba-based approach could be related to the complexity and interpretability of the model. As the network architecture becomes more sophisticated, it may become challenging to interpret the learned features and understand the decision-making process of the model. To address this, techniques such as visualization methods, attention mechanisms, or explainable AI approaches can be integrated to provide insights into the model's inner workings. Additionally, ensuring robustness to noisy or incomplete data is crucial for real-world applications. Regularization techniques, data augmentation, and robust loss functions can help improve the model's generalization capabilities and performance on unseen data. Furthermore, optimizing hyperparameters, such as learning rates and regularization terms, through thorough experimentation and tuning can enhance the model's overall performance.