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Kumari, Sweta (2021) Classification of PCOS/PCOD Using Transfer Learning and GAN Architectures to Generate Pseudo Ultrasound Images. Masters thesis, Dublin, National College of Ireland.
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Abstract
Polycystic Ovarian Syndrome(PCOS) is one of the most worrying concerns for women in the 21st century. Undiagnosed and treatable for long periods of time can be fatal. As deep learning is a rapidly emerging technology in medical diagnosis and various neural network have produced promising results in image classification and detection. However, classifying Polycystic ovarian syndrome in ovary ultrasonic images automatically was motive as implemented in other diagnosis such as X-Rays, MRI, CT Scan etc. using CNN and other neural networks. Another progress in computation vision has been made by implementing a simple GAN model to generate synthetic images in order to overcome the lack of dataset then also data augmentation on re-processed ultrasonic images with resizing and finally trained the models. Among all the implemented model namely VGG-19, DenseNet-121, ResNet-50 and Inception V3 and model stacking, highest accuracy with better sensitivity and specificity is achieved by VGG-19 i.e. approximately 70%.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Deeplearning; Medical Diagnosis; Polycystic ovarian syndrome(PCOS); transfer learning; GAN; data augmentation |
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software T Technology > T Technology (General) > Information Technology > Computer software R Medicine > R Medicine (General) H Social Sciences > HM Sociology > Information Science > Communication > Medical Informatics |
| Divisions: | School of Computing > Master of Science in Data Analytics |
| Depositing User: | Clara Chan |
| Date Deposited: | 06 Dec 2021 15:37 |
| Last Modified: | 06 Dec 2021 15:37 |
| URI: | https://norma.ncirl.ie/id/eprint/5181 |
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