CMP7200 Individual Master’s Project Example

Introduction

The dissertation analyzes the possibility of early lung cancer detection by chest Computed Tomography (CT) using Convolutional Neural Networks (CNNs) in comparison with conventional methods of diagnosis. Lung cancer remains one of the most deadly forms of cancer because there is a lag in diagnosis; hence, the necessity for more advanced techniques to diagnose early on.

This study, therefore, aims to evaluate the diagnostic performance, sensitivity, and speed of CNNs in detecting early-stage lung cancer. It uses quantitative methods in which it trains a CNN model and assesses its performance against chest CT images, comparing its performance with conventional diagnostic methods in terms of sensitivity and specificity. The research mentions how CNNs can improve the power, speed, and objectivity of diagnostics, but also mentions issues that might hinder their introduction on a large scale into clinical practice, such as model quality, usability, and integration. It emphasizes the impact of AI in modern healthcare and points out areas for future research to help improve AI driven medical diagnostics.

Table of Contents 

Abstract:
List of Figure

Chapter 1: Introduction

1.1 Background and Context
1.2 Research Rationale
1.3 Research Aim, Objectives, and Questions
1.3.1 Aim of the Study
1.3.2 Objectives
1.3.3 Research Questions
1.4 Research Hypotheses
1.5 Scope of the Study 
1.6 Structure of the Dissertation 

Chapter 2: Literature Review

2.1 Chapter Introduction 
2.2 Traditional Methods of Cancer Detection in Chest CT Scans
2.2.1 Manual Interpretation by Radiologists
2.2.2 Computer-Aided Detection (CAD) Systems
2.3 Convolutional Neural Networks (CNNs) in Medical Image Analysi
2.3.1 Introduction to CNNs
2.3.2 CNN Applications in Medical Imaging
2.4 Comparative Studies of CNNs and Traditional Methods
2.4.1 Accuracy and Reliability 
2.4.2 Speed and Efficiency 
2.4.3 Sensitivity and Specificity 
2.5 Challenges and Limitations of CNNs in Early Cancer Detection
2.5.1 Data Requirements and Quality
2.5.2 Interpretability and Transparency 
2.5.3 Clinical Integration and Acceptance
2.6 Future Directions and Potential Improvements
2.6.1 Advances in CNN Architectures
2.6.2 Enhancing Data Annotation and Quality 
2.6.3 Integrating CNNs with Other Diagnostic Tools
2.7 Chapter Summary

And more...

Conclusion and Recommendation 

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