AWS Certified Cloud Practitioner

Explanation

Transfer learning is the most efficient approach in this scenario because:

• The hospital already has a trained model for pneumonia detection from chest X-rays

• Both tasks (pneumonia detection and lung cancer detection) use the same:

  • Base architecture

  • Dataset type (chest X-rays)

  • Domain (medical imaging)

Why transfer learning works best:

1. Reuses learned features: The model has already learned relevant features from chest X-rays that are useful for both pneumonia and lung cancer detection

2. Faster training: Only the final layers need to be retrained or fine-tuned, significantly reducing training time

3. Less data required: Transfer learning typically requires less labeled data for the new task

4. Better performance: Leveraging pre-trained weights often leads to better performance compared to training from scratch

Why other options are less efficient:

• Unsupervised learning: Would require learning patterns from scratch without labels

• Self-supervised learning: Involves creating pseudo-labels, which is unnecessary when you already have a relevant pre-trained model

• Reinforcement learning: Not suitable for this type of classification task; designed for sequential decision-making problems

Transfer learning allows the hospital to build upon their existing investment in the pneumonia detection model, making the adaptation to lung cancer detection much more efficient and effective.