Study and detection of PCOS related diseases using CNN. Issue 1 (February 2021)

• Record Type: Journal Article
• Authors: Sumathi, M; Chitra, P; Sakthi Prabha, R; Srilatha, K
• Is Part Of: IOP conference series. Volume 1070:Issue 1(2021)
• Publication Date: 2021-02
• Subjects: CNN -- PCOS related diseases -- Deep learning -- Medical image processing.; Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/1070/1/012062
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: Polycystic ovary syndrome (PCOS) is a group of symptoms caused by high levels of androgens in women. The cause of PCOS is a group of genetic and environmental factors that are common pathologies, often associated with clinical symptoms of arteries, hirsutism, acne, and hyperandrogenism, along with chronic infertility. Recent studies show that about 18% of Indian women suffer from this syndrome. Doctors were manually examining ultrasound images and conclude the affected ovary but unable to find whether it is a simple cyst, PCOS, or cancer cyst. In this paper, CNN based algorithms proposed and coding developed in Python programming for classification of cysts, and they are filled with blood or fluid using ultrasound images. The study is performed on CNN based image processing feature extraction to classify cysts in the dataset. That is the study is carried out using an independent trained dataset of the same PCOS related diseases. Finally, the test dataset is used for performing the feature extraction process and the results are met with 85% accuracy using performance factors.