Identification And Classification of PCOD &PCOS Using Machine Learning Techniques

Abstract— Polycystic Ovarian Disease (PCOD) is a medical condition in which a woman's ovaries generate immature or partially mature eggs in large numbers during reproductive age. These eggs develop into cysts in the ovaries over time. Due to the cysts, ovaries become large & secrete large amounts of male hormones (androgen) which causes infertility problems, irregular periods, unwanted weight gain, and other health issues. Polycystic Ovary Syndrome (PCOS) is a condition that affects girl or women during their child-bearing years and disturbs the levels of hormones. This disturbance results in problems affecting many body systems. Women having PCOS have skip or irregularity in menstrual periods as well as cysts formation in the either or both ovaries. Symptoms of PCOS and PCOD are irregular periods, excess androgen, polycystic ovaries, abnormal BMI, disturbed levels of hormones (LH, FSH, DHEAS), poor insulin resistance. But as per research studies, these symptoms are not sufficient for accurate detection for diverse data. This proposed system will verify the possibilities of PCOS and PCOD based on their symptoms and confirms it by classifying Ultrasound ovary images using deep learning methodology. The ultra-sound ovary image analysis along with the physical symptoms of PCOS and PCOD are needed for accurate detection and reducing number of outliers during analysis. Such detection of PCOS and PCOD also helps in proper treatment and reducing the health loss. Keywords—Symptoms based form for PCOD/PCOS, Ultrasound image processing ,Precautions.