Chapter 1: Introduction
This thesis examines machine learning applications in diagnostic imaging.

Chapter 2: Literature Review
CNNs have achieved 94% accuracy in tumor detection (Smith et al., 2023).
Transfer learning approaches show promise for medical imaging (Jones, 2024).

Chapter 3: Methodology
We propose a hybrid CNN-Transformer architecture with 12 attention heads.
Training uses the MIMIC-CXR dataset with data augmentation.

Chapter 4: Results
The hybrid model achieved 97.2% accuracy, outperforming pure CNN (94.1%)
and pure Transformer (95.8%) baselines on the test set.
