You can spilt your data based on the strategy of stratification, this way your training set will have equal number of examples for both of these classes, this would be easy to implement for tabular data since Sklearn has the provision to do this in the train_test_split(). Coming to the stratified split of image data, I fear there is no library that implements it, so you have to do it yourself.

Solving class imbalance in segmentation data for a deep learning model is essential to ensure that the model does not bias its predictions toward the majority class. Imbalanced data can lead to poor segmentation performance, where the model may struggle to identify and classify the minority class correctly. Here are several strategies to address class imbalance in segmentation data:

1. **Data Augmentation**:

- Augment the minority class samples by applying random transformations such as rotations, translations, scaling, and flipping. This can help increase the diversity of the minority class data.

2. **Resampling Techniques**:

- **Oversampling**: Increase the number of samples in the minority class by duplicating existing samples or generating synthetic samples. Techniques like SMOTE (Synthetic Minority Over-sampling Technique) can be used to create synthetic samples that are similar to the minority class.

- **Undersampling**: Reduce the number of samples in the majority class to balance the class distribution. However, be cautious with undersampling, as it can lead to loss of important information.

3. **Weighted Loss Function**:

- Modify the loss function of your deep learning model to assign higher weights to the minority class. This gives more importance to correctly classifying the minority class during training.

4. **Patch-Based Training**:

- Instead of training on entire images, divide the images into smaller patches and balance the class distribution within each patch. This can help the model learn the minority class better.

5. **Transfer Learning**:

- Utilize pre-trained models on large datasets (e.g., ImageNet) and fine-tune them on your segmentation task. Transfer learning can help your model learn useful features even with limited data.

6. **Use Multiple Models**:

- Train multiple models with different initializations or architectures and combine their predictions. This can help in reducing the bias towards the majority class.

7. **Data Collection**:

- If possible, collect more data for the minority class. A larger and balanced dataset can often alleviate class imbalance issues.

8. **Change Evaluation Metrics**:

- Consider using evaluation metrics that are less sensitive to class imbalance, such as the Intersection over Union (IoU) or Dice coefficient, instead of accuracy.

9. **Post-processing**:

- After segmentation, post-process the results to further refine the predictions. Morphological operations like erosion, dilation, and connected component analysis can help clean up the segmentation masks.

10. **Ensemble Methods**:

- Combine predictions from multiple models, which may have been trained with different strategies, to improve overall segmentation accuracy.

It's essential to choose the most appropriate strategy based on the specifics of your dataset and the problem at hand. Experiment with different approaches and evaluate the performance of your deep learning model using appropriate validation techniques to ensure that the class imbalance is effectively addressed without introducing other issues.

Hello,

Usually, there is better to use complex approach.

According to the second point, I can suggest to try Focal Tversky Loss Function (Article Tversky loss function for image segmentation using 3D fully ...