Chapter 1: Introduction

This thesis explores the application of machine learning in healthcare.
We focus on diagnostic imaging and patient outcome prediction.

Chapter 2: Literature Review

Previous work has shown that CNNs can detect tumors with 94% accuracy.
Recent studies also demonstrate transformer models achieving 97% on the same task.

Chapter 3: Methodology

We propose a hybrid CNN-Transformer architecture.
The model combines spatial feature extraction with attention mechanisms.

Chapter 4: Results

Our model achieved 98.5% accuracy on the test dataset, outperforming
both pure CNN and pure transformer baselines.

Chapter 5: Conclusion

The hybrid approach shows promise for clinical deployment.
Future work should focus on interpretability.
