University of Twente Student Theses

Login

Machine learning and deep learning for better assessment of the Big-3 disease in healthcare

Hofmeijer, E.I.S. (2020) Machine learning and deep learning for better assessment of the Big-3 disease in healthcare.

[img] PDF
7MB
Abstract:B3CARE aims to advance the technology readiness level for screening of the three diseases, lung cancer, Chronic Obstructive Pulmonary Disease (COPD) and Cardiovascular Disease (CVD), by accelerating software development for data post-processing of CT-images. A focus is put on one task in this project: Development of simulator of patient cases. Training of both the physician and of advanced software is reliant on the amount and variability of patient cases. Therefore, it would be desirable to create them. The objective of the thesis is to investigate to what extent a generative model, a Generative Adversarial Network (GAN), can be created and used to simulate chest-CT patient cases with benign or malignant nodules. This will be accomplished through answering three sub-questions. It will first be considered in what way images can be created and how the type of image can be influenced. Following this, it will be explored in what way the quality of the generated images can be quantitatively assessed and if this corresponds to human judgement. Finally, it will be explored to what degree lung nodules can be recognised and classified using advanced software tools. Lung nodules are created successfully, but their quantitative assessment is still a point of discussion.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:31 mathematics, 44 medicine, 50 technical science in general
Programme:Biomedical Engineering MSc (66226)
Link to this item:http://purl.utwente.nl/essays/80395
Export this item as:BibTeX
EndNote
HTML Citation
Reference Manager

 

Repository Staff Only: item control page