Early Stage Detection of PCOS Using Deep Learning

A medical illness known as polycystic ovary syndrome (PCOS) af fects hormones in women who are fertile. A delayed or nonexistent menstrual cycle is caused by hormonal imbalance. Women who have PCOS typically struggle with significant symptoms, including weight gain, facial hair growth, pimple, hair loss, and irregular periods. In some cases, these symptoms may lead to infertility. PCOS should be diagnosed and treated as early as possible because it frequently coexists with adiposity, hyperglycemia, and hypercho- lesterolemia. Since there are no direct images and it is challenging to extract the information needed from an image in order to detect PCOS, the current ap proaches and treatments are inadequate for early-stage detection and prediction utilizing numerical datasets. This issue is addressed by this chapter, which proposes a method for early diagnosis of PCOS, which makes use of ultrasound images of the ovaries. Inception V3 and Sequential CNN are two deep learning convolution neural network methods that are used. In order to predict PCOS, the best algorithm, Sequential CNN, is used, and these methods are contrasted. In this work, an image classification system is used to automatically identify PCOS from the input ovarian ultrasound picture.