Detection And Prediction System For Polycystic Ovary Syndrome Using Structural Normalized Square Similarity Detection Approach

Abstract

Poly Cystic Ovary Syndrome (PCOS) is the most common endocrine disorders affected to female in their reproductive cycle. It seriously affects women's health. It is observed that PCOS is a condition seen among the women of reproductive age is having a major influence in the cause of infertility. Over five million women worldwide in their reproductive age got affected by PCOS. It is an endocrine disorder characterized by changes in the female hormone levels and the abnormal production of male hormones. This condition leads to ovarian dysfunction with increased risk of miscarriage and infertility. The symptoms of PCOS include obesity, irregular menstrual cycle and excessive production of male hormone, acne and hirsutism. It is extremely difficult to diagnose PCOS due to the heterogeneity of symptoms associated and the presence of a varying number of associated gynaecological disorders. The objective is detecting the follicles using object growing method. It consists of two major stages including pre-processing phase and follicle identification based on object growing method. To address this problem, this paper proposes a system for the early detection and prediction of PCOS, by applying the Structural Normalized Square Similarity Detection Approach (SNSSDA).