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Tiwari, Deepti Deepak (2023) PolyCystic Ovary Syndrome Detection Using CapsuleNet and Synthetic Data. Masters thesis, Dublin, National College of Ireland.
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Abstract
Today 6% to 12% of women who are of their reproductive age suffer from a common endocrine disorder known as PolyCystic Ovary Syndrome (PCOS) which is the most common cause of infertility in females. Out of these only a few get diagnosed and undergo proper treatment. The challenge is to detect PCOS on the base of infected and non-infected ultrasound of ovaries. This research proposed a framework which used deep learning methodologies and data augmentation for PCOS detection. The model implemented in this research is a capsule network to overcome the drawback of traditional CNN model with synthetic data later, the model was tuned to achieve better results. The dataset used here consists 3856 images of pelvic region ultrasound which can be categorized into two categories. Later the model is evaluated on accuracy, F1 score, recall, sensitivity, loss function training and test accuracy etc. This research demonstrates promising results that can be achieved to detect PCOS using Capsule network and synthetic data. This model can be used in medical field to detect PCOS at early stage which can mitigate future complications.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Nayak, Prashanth UNSPECIFIED |
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science R Medicine > RG Gynecology and obstetrics Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning |
| Divisions: | School of Computing > Master of Science in Data Analytics |
| Depositing User: | Tamara Malone |
| Date Deposited: | 08 Jan 2025 17:04 |
| Last Modified: | 08 Jan 2025 17:04 |
| URI: | https://norma.ncirl.ie/id/eprint/7284 |
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