Category | Dissertation | Subject | Computer Science |
---|---|---|---|
University | Birmingham city university | Module Title | CMP7200 Individual Master’s Project Handbook |
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.
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