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Machine learning and deep learning for better assessment of the big-3 diseases in health care

Hofmeijer, E.I.S. (2019) Machine learning and deep learning for better assessment of the big-3 diseases in health care.

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Abstract:The research project 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. To realise widespread implementation of screening for these three diseases, it is necessary to inform/train (technical) physicians on how to exploit computer assistance based on post-processing software like machine- and deep learning. In this thesis, there will be a focus on the training and education work package of the B3CARE project to prepare (technical) physicians to be able to understand and use advanced software tools in clinical care. A focus is put on one particular task in this work package: The development of a simulator of patient cases. Since both the training of physicians and the training of the advanced software tools rely on the amount and variability of patient cases, it is desirable to be able to create them. The objective of this thesis will be to investigate to what extent a generative model, especially a Generative Adversarial Network (GAN), can be created to simulate chest CT patient cases with benign or malignant nodules. This will be accomplished through answering three consecutive sub-questions. It will first be considered in what way images can be created and how the type of image and its difficulty 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. Both these topics will be handled using an image data set of hands with six different classes. Every class will correspond to the number of fingers that is raised by the hand. This data set is chosen, considering that the average person should be an “expert” in hands, and it will therefore be easier to qualitatively assess the generated images. Finally, it will be explored to what degree lung nodules can be recognised and classified using an algorithm. To accomplish this, first of all lung nodules are created using the knowledge obtained in the first two topics. It could be concluded that conditional GANs (cGANs) are a good method to generating new images of which the type can be influenced. However, influencing this type is only possible if the data set used to train with, is already classified according to some measure of type. It proved to be difficult to quantitatively assess the generated images. Metrics to do so exist and three of them have been explored to see to what extent they correlate with human judgement. The biggest problem with two of these metrics is that they are dependent on the performance of the classifier that they use. Also, two of the metrics are influenced by the ratio of classes occuring in the generated image data sets. Using a classifier trained on a real nodules image data set, the generated nodule image test set received an accuracy of 73%, whereas the real nodule image test set received an accuracy of 70%. This difference might be because the cGAN has only learned some basic features of benign and malignant nodules which makes it easier for the independent classifier to classify them.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Programme:Electrical Engineering MSc (60353)
Link to this item:http://purl.utwente.nl/essays/80948
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