Automated detection of Polycystic Ovarian Syndrome using follicle recognition

Polycystic Ovarian Syndrome (PCOS) is one of the most common hormonal disorder present in females in reproductive age group. Early detection and treatment of PCOS is important since it is often associated with obesity, type 2 diabetes mellitus, and high cholesterol levels. In this paper, automated detection of PCOS is done by calculating no of follicles in ovarian ultrasound image and then incorporating clinical, biochemical and imaging parameters to classify patients in two groups i.e. normal and PCOS affected. Number of follicles are detected by ovarian ultrasound image processing using preprocessing which includes contrast enhancement and filtering, feature extraction using Multiscale morphological approach and segmentation. Support Vector Machine algorithm is used for classification which takes into account all the parameters such as body mass index (BMI), hormonal levels, menstrual cycle length and no of follicles detected in ovarian ultrasound image processing. The results obtained are verified by doctors and compared with manual detection. The accuracy obtained for the proposed method is 95%.