Breast Cancer Classification with PCA

Dec 14, 2024

Breast Cancer Classification using PCA and Deep Learning

Introduction

  • Breast Cancer: A significant health concern worldwide.
  • Objective: Develop a system for classifying breast cancer.
  • Methods: Principal Component Analysis (PCA) and Deep Learning.

Principal Component Analysis (PCA)

  • Purpose: Dimensionality reduction technique.
  • Benefits:
    • Reduces computational cost.
    • Helps in better visualization of data.
  • Process:
    • Identify the main components of data.
    • Reduce dimensions while retaining most information.

Deep Learning

  • Introduction:
    • Mimics human brain functions.
    • Involves neural networks with multiple layers (deep networks).
  • Application in Breast Cancer:
    • Process medical images.
    • Extracts hidden features that indicate presence of cancer.

Integration of PCA and Deep Learning

  • Synergy: PCA reduces dimensions, while deep learning handles classification.
  • Workflow:
    • PCA first reduces dataset size.
    • Deep learning model further analyzes the reduced dataset.

Results and Discussion

  • Effectiveness: PCA and deep learning combination increases efficiency.
  • Accuracy: Achieves high accuracy in distinguishing cancerous from non-cancerous samples.
  • Challenges: Requires significant computational resources.

Conclusion

  • Summary: PCA and deep learning are effective in breast cancer classification.
  • Future Work: Explore more deep learning architectures and optimization techniques.

References

  • Research Papers: Extensive literature review supporting PCA and deep learning methods.

This summary captures the essence of the presented lecture, outlining key methods and findings related to the application of PCA and deep learning in the classification of breast cancer.