Lung Cancer Detection Using Machine Learning and Deep Learning

Kutaphale, Yash Santosh (2023) Lung Cancer Detection Using Machine Learning and Deep Learning. Masters thesis, Dublin, National College of Ireland.

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Abstract

Machine learning (ML) and deep learning (DL) have been combined to create a new way to diagnose lung cancer, which is the top cause of death around the world. In order to improve the accuracy of medical CT scan interpretation and solve issues brought on by the complex nature of these scans, this research presents a novel method that combines deep learning and conventional machine learning. Our work, used a large set of CT pictures from different groups of people in central Iraq. To this dataset, two different techniques were used. The initial stage focused on the cooperative interaction of several ML algorithms, highlighting the significance of careful data calibration. This served as the foundation for a later, more sophisticated approach built on a Convolutional Neural Network (CNN) originally intended for the classification of retinal images. The training accuracy for this CNN methodology was astonishingly high at 98.89% after balancing the data using SMOTE. Our study's central thesis is that combining these computer models with conventional diagnostic techniques has the potential to transform lung cancer diagnosis by giving radiologists effective tools for early and precise identification. Additionally, we demonstrate the revolutionary potential of AI in transforming the landscape of medical picture analysis by contrasting our techniques with current studies.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Rustam, Furqan UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science R Medicine > RB Pathology R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer) Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence > Computer vision Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence > Computer vision Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Tamara Malone
Date Deposited:	29 Nov 2024 12:42
Last Modified:	29 Nov 2024 12:42
URI:	https://norma.ncirl.ie/id/eprint/7209

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