AWS Certified Cloud Practitioner

Explanation

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

1. Reuse of learned features: The model already trained on chest X-rays for pneumonia detection has learned relevant features about chest anatomy, X-ray patterns, and medical imaging characteristics.

2. Same domain: Both tasks (pneumonia detection and lung cancer detection) use the same type of data (chest X-rays) and similar visual patterns, making the transfer highly effective.

3. Reduced training time: Instead of training a model from scratch, transfer learning allows you to:

   • Start with the pre-trained pneumonia detection model

   • Fine-tune only the final layers for the new task (lung cancer detection)

   • Use the learned feature extraction capabilities

4. Less data required: Transfer learning typically requires less labeled data for the new task compared to training from scratch.

Why other options are incorrect:

• A) Unsupervised learning: This involves finding patterns in unlabeled data without specific task guidance, which is not efficient for adapting a model to a specific new classification task.

• B) Self-supervised learning: This involves creating supervisory signals from the data itself, which is useful for pre-training but not the most efficient for adapting an already-trained model to a related task.

• D) Reinforcement learning: This involves learning through trial-and-error with rewards, which is not suitable for medical image classification tasks and would be highly inefficient compared to transfer learning.

Practical application:

In practice, the hospital would:

1. Take the pre-trained pneumonia detection model

2. Replace the final classification layer(s) for lung cancer detection

3. Fine-tune the model on a smaller dataset of lung cancer X-rays

4. Benefit from faster convergence and better performance than training from scratch