Automated Detection of Polycystic Ovarian Syndrome using Neural Network Algorithm

Abstract

The ovarian ultrasound imaging could be a good tool in infertility treatment. Monitoring the follicles is extremely important in human reproduction. Periodic measurements of the scale and shape of follicles over several days are the primary means of evaluation by doctors. Today monitoring the follicles is finished by non-automatic means with human interaction. This work could also be very demanding and inaccurate and, in most of the cases, means only an additional burden for doctors. The detection of PCOS is often done using blood tests and ultrasound images of the ovaries that detect multiple follicles. This provides the accurate results of if women are suffering PCOS or not. The proposed methodology goes to be accustomed detect multiple follicles that indicate the sign of PCOS. Using image-preprocessing techniques we'll be enhancing the quality of the image to make it ready for the classification phase. Neural network algorithms that are best for such processed have gotten accustomed classify the syndrome. The experimental results demonstrate the efficiency of the tactic.

Keywords: Multiple Follicles, ultrasound images, neural network algorithm, technology, PCOS (Polycystic Ovarian Syndrome).

Cite this Article: Deeksha Manish Singh, Kardam Hitendra Acharya, Omkar Eknath Mestry, Arti Gore. Automated Detection of Polycystic Ovarian Syndrome using Neural Network Algorithm. Recent Trends in Parallel Computing. 2020; 7(2): 9–13p.