| Category | Dissertation | Subject | Computer Science |
|---|---|---|---|
| University | Birmingham city university | Module Title | CMP7200 Individual Master’s Project Handbook |
This dissertation concerns the feasibility of the CNNs in early cancer diagnosis by CT chest relative to conventional image analysis methods. Lung cancer is one of the leading cancer types when it comes to fatalities mostly because of late diagnosis hence a need for better detection techniques. The research question of this study explores whether CNNs can increase the diagnostic accuracy, sensitivity and the speed in the diagnosis of early-stage lung cancer than the conventional CAD employed by radiologists and in CAD systems. ..........View More
Abstract:
List of Figure
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
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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