Artificial intelligence applications in medical imaging
Artificial intelligence applications in medical imaging
| datacite.contributor.Supervisor | Αριστείδης Δερμιτζάκης | |
| datacite.rights | http://purl.org/coar/access_right/c_abf2 | el |
| dc.contributor.author | Θεοδωρής, Νικόλαος | |
| dc.contributor.other | Theodoris, Nikolaos | |
| dc.date.accessioned | 2022-06-24T08:48:56Z | |
| dc.date.available | 2022-06-24T08:48:56Z | |
| dc.date.copyright | 2022-06-23 | |
| dc.degree | masterThesis | el |
| dc.description.abstract | Breast cancer is the number one cancer that affects women and cause millions of deaths. Modern technology and engineering have developed many CAD systems to increase the survival rate of women with breast tumors and help the specialists to diagnose more easily the illness. This thesis is about the development of a computer aided diagnosis system that is responsible for the detection and the segmentation of breast cancer in digital mammograms. Initially, the breast cancer is analyzed in terms of its anatomy and the reasons that cause it. More specifically, the symptoms and the classification of breast cancer is crucial in our work. There are also many ways to diagnose this illness and they are described. In the next chapter, the basics of neural networks, machine learning and convolutional neural networks are explained. CAD systems use neural networks for classification, detection, or segmentation of breast cancer. For this reason, we describe how they work. A state-of-the-art report is also done for the neural networks that are used in medical imaging of breast cancer. Moreover, my UNet development for breast cancer segmentation of InBreast database is presented. First, the software and the materials that I used to create the system for breast imaging are referred. The techniques of image preprocessing, data augmentation and the UNet architecture are described with the analysis of the Python code. Furthermore, I compare the results of the UNet development from different experiments, where I changes the hyperparameters. The prediction results of breast cancer binary masks and the evaluation metrics that I have used to compare them are presented in this chapter. Finally, a summary of the conclusions that emerged from this research is made and some possible future modifications and ideas are explored. | el |
| dc.identifier.uri | http://hdl.handle.net/10889/16283 | |
| dc.language.iso | en | el |
| dc.subject | Καρκίνος μαστού | el |
| dc.subject | Νευρωνικά δίκτυα | el |
| dc.subject | Τμηματοποίηση όγκου | el |
| dc.subject | Ιατρική απεικόνηση | el |
| dc.subject.alternative | Breast cancer | el |
| dc.subject.alternative | Neural networks | el |
| dc.subject.alternative | Tumor segmentation | el |
| dc.subject.alternative | Medical imaging | el |
| dc.title | Artificial intelligence applications in medical imaging | el |
| dc.title.alternative | Εφαρμογές τεχνητής νοημοσύνης στην ιατρική απεικόνιση | el |
| oaire.licenseCondition | null | el |