부산대학교 도서관

Polycystic ovary syndrome (PCOS) is a hormonal endocrine disorder that infect many women in their reproductive cycle. It is a concern in a married couple because it is related fertility rate of women. One of the criteria for diagnosing PCOS are polycystic ovaries (PCO). Polycystic ovaries can be seen from the number and diameter of each follicle on ultrasound image. In previous studies, there are existing PCO classifications done automatically by the system using several methods. However, on those studies its feature extraction of the ultrasound image is still done manually. In this research, we propose a solution where the feature extraction is also done automatically using Convolutional Neural Network. CNN provide the best test performance with micro-average f1-score of 100% and an average of 76.36% on a 5-fold cross-validation.