Generative Adversarial Network for Generation of Artificial Microwave Data for Stroke Detection
Typ
Examensarbete för masterexamen
Program
Complex adaptive systems (MPCAS), MSc
Publicerad
2021
Författare
Ekblom, Ebba
Svensson, Rebecca
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
This study aims to explore the possibilities of generating microwave data with a
Generative Adversarial Network (GAN), in order to expand the existing data set
and increase the performance of a stroke detection algorithm. Key challenges of
the project relate to the small data set size and samples with many features. The
generation of data was done with a Conditional Wasserstein Generative Adversarial
Network. Due to the low data regime, the effects of adding DeLiGAN was also
investigated. In addition to generating data with a GAN, this study also covers
methods for the evaluation of generated data. To evaluate the quality of the generated
data, a separate classifier network is utilised.
Evaluation of the generated data in classification problems, as well as visualisation
of distribution coverage, indicate that the data is of good quality and represent
the distribution of original data well. However, results also show that the generated
data cannot completely substitute the real data, and is deemed to be lacking
in some quality measure. Still, the results are promising and the project concludes
that it certainly is possible to generate microwave data which is to be used for stroke
detection, with great potential for further improvements.
Beskrivning
Ämne/nyckelord
Generative Adversarial Networks , Wasserstein GAN , Conditional GAN , DeLiGAN , microwave , haemorrhagic stroke