What action can most likely improve the generalization of a deep learning model analyzing medical images?

Applying data augmentation techniques is an effective method to improve the generalization of a deep learning model, particularly in the context of analyzing medical images. Data augmentation involves dynamically altering the training data by applying transformations such as rotation, flipping, scaling, cropping, and adjusting brightness or contrast. This process helps create a more diverse dataset without the need for collecting more images, thus enhancing the model's ability to learn robust features.

By exposing the model to a variety of altered images, it becomes less likely to overfit to specific patterns or noise present in the training set. Instead, the model learns to generalize better across unseen data, which is crucial in medical imaging where variations in anatomy, imaging conditions, and other factors can occur. Overall, data augmentation enriches the training process and significantly contributes to improving the model’s performance on real-world data.

In contrast, increasing the complexity of the neural network architecture may lead to overfitting, especially if the model becomes too tailored to the training data. Reducing the number of training epochs might prevent the model from learning sufficiently from the data, resulting in poor performance. Lastly, while increasing the batch size can influence training dynamics, it does not inherently contribute to improving the model's generalization capability the way data augmentation does