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Introduction
Girls and women of reproductive age are more susceptible for a medical disorder referred to as polycystic ovarian syndrome (PCOS), which presents with a range of signs. Changes in menstrual cycle, abnormal face and body hair development, acne, obesity, the existence of ovarian cysts, and infertility are a few of these signs. It is a complicated, incurable disorder caused by a hormonal imbalance that typically has an impact on how the ovaries function. Future development of various health issues such as type 2 diabetes, gestational diabetes, high cholesterol, high blood pressure, sleep apnea, and stroke is quite likely in women with PCOS. According to studies 1, 3-10% of people in an undefined population have PCOS.2 PCOS and endometriosis are the two reproductive illnesses that affect women the most frequently. PCOS is characterized by polycystic ovaries, hyperandrogenism, and ovulatory dysfunction. In conjunction with other syndrome symptoms, hyper androgenism is acknowledged as a critical diagnostic factor (1). Different diagnostic methods are used in different parts of the world. National Institutes of Health (NIH) states that the presence of both hyperandrogenism and olgio/amenorrhea can be used to diagnose PCOS (2). Depending on diagnostic standards, PCOS incidence varies.3 About 7% of reproductive-aged women have hyper androgenic chronic an ovulation. One polycystic ovary is adequateto make diagnosis of PCOS according to Rotterdam criteria (3), which stipulate that the polycystic ovary must be ultrasonically visible. The prevalence of PCOS increases from 55% to 91% in women with norm gonadotropic an ovulation when using the NIH criteria (5). Secondary causes should be ruled out before primary causes, according to all diagnostic strategies. Machine learning (ML) techniques for PCOS identification have promise.4 Using image data, a ML method applied to ovarian ultrasound scans could accurately diagnose PCOS (6). Although the usage of intelligent programmes in medicine and healthcare is growing, a strong and focused strategy is still required. Targeting patients or medical professionals has demonstrated enormous potential for machine learning.5 Convolutional neural networks (CNN) and recurrent neural networks (RNN), which are used in machine learning techniques such as DL, the diagnosis can be made more accurately while the system can be made simpler. With the aid of annotated training data, these techniques can be widely utilised...