To improve segmentation accuracy, especially for images with low contrast between regions of interest, exploring other deep learning architectures and techniques can be beneficial. One promising approach is to implement a hybrid model that combines multiple architectures, such as a U-Net for feature extraction and a Mask R-CNN for precise segmentation. This fusion of architectures can leverage the strengths of each model to enhance the overall segmentation performance. Additionally, techniques like data augmentation, transfer learning, and ensemble learning can be employed to boost the model's generalization capabilities and robustness. Data augmentation techniques like rotation, scaling, and flipping can help create a more diverse training dataset, enabling the model to learn from a wider range of variations in the input data. Transfer learning, by utilizing pre-trained models on large image datasets, can expedite the training process and improve performance on smaller datasets. Ensemble learning, where multiple models are combined to make predictions, can further enhance segmentation accuracy by leveraging the collective intelligence of diverse models.