Project Title

Harnessing GAN for the Prediction of Asthma through Radiographic Image Analysis

Authors

• Samar Ahmed (ID: 2596741)
• Brintha Thirunavukkarasu (ID: 2555470)
• Sarah Nkembi (ID: 2616424)
• Dhruvi Vekariya (ID: 2590401)

Project Overview

This project explores the use of Generative Adversarial Networks (GANs) for predicting asthma through the analysis of radiographic images. Asthma is a prevalent long-term medical condition that is often misdiagnosed due to its similarity to other respiratory conditions. This project aims to augment medical image datasets with synthetic images generated by GANs, enhancing the accuracy of early asthma diagnosis.

Key Sections

1. Abstract: Summary of the research background, aims, and methodology.
2. Introduction: Overview of asthma prevalence and the importance of early diagnosis.
3. Selection of GAN: Methodology: Explanation of the GAN model and its applications in medical image augmentation and anomaly detection.
4. Justification of GAN Technology: Benefits of using GANs for bulk image generation in medical settings.
5. Discussion of the Analysis: Analysis of potential challenges and considerations, including trust issues, ethical, legal, societal, and algorithmic aspects.
6. Reflections of LLM: Insights from large language models on the implementation and impact of the proposed methodology.
7. Conclusion: Summary of findings and future implications.
8. References: Comprehensive list of sources and research materials.
9. Appendix: Future Work: Suggestions for further research and development.

Usage

This document is intended for academic purposes, providing a detailed examination of the potential of GANs in improving asthma diagnosis. The insights and methodologies discussed can serve as a foundation for future research and practical applications in medical imaging and artificial intelligence.