Sunny, Amal (2024) Deep Learning-Based Lung Cancer Detection and Classification from Medical Images. Masters thesis, Dublin, National College of Ireland.
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Abstract
Lung cancer is one of the most common causes of cancer-related deaths, and early detection is critical for effective treatment. However, traditional diagnostic methods that include manual inspection of the medical imaging slides have limitations in their accuracy and can be time-consuming. To overcome these challenges, deep learning approaches like convolutional neural networks (CNN) have shown great potential for providing fast, accurate, and noninvasive diagnostic tools. However, to improve the accuracy and efficiency of these models, a standardised image pre-processing technique is necessary. Such techniques could help reduce the cost and time associated with traditional diagnostic methods. By leveraging the power of deep learning, we can enhance the accuracy of lung cancer diagnosis, reduce the need for invasive tests, and ultimately improve patient outcomes. The empirical evidence from this investigation underscores the superior performance of the InceptionNetV3 model, and VGG19 model, achieving a 84.47%, and 85.84% accuracy rate in lung cancer detection. These findings not only underscore the significance of model selection in deep learning applications but also illuminate the path for further refinements in automated diagnostic technologies.
Item Type: | Thesis (Masters) |
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Supervisors: | Name Email Nagahamulla, Harshani UNSPECIFIED |
Uncontrolled Keywords: | Lung Cancer Detection; CNN; InceptionNet; DenseNet121; EfficientNet; VGG16; VGG19; Medical Imaging; Automated Diagnostic Systems |
Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science R Medicine > Healthcare Industry Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HM Sociology > Information Science > Communication > Medical Informatics |
Divisions: | School of Computing > Master of Science in Data Analytics |
Depositing User: | Ciara O'Brien |
Date Deposited: | 05 Jun 2025 13:54 |
Last Modified: | 05 Jun 2025 13:54 |
URI: | https://norma.ncirl.ie/id/eprint/7763 |
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