Polycystic ovary syndrome is a hormonal disorder seen in many women. It occurs by the combination of many small and benign cysts in the ovaries. These cysts, called follicles, create a special pattern in the ovaries observed with ultrasound imaging. The number, structure, and size of these follicles provide important information for the diagnosis of ovarian diseases. In this study, two different methods of follicle detection are tested for Polycystic Ovary Syndrome. The first method consists of noise filtering, contrast adjustment, binarization, and morphological processes. For this method, Median Filter, Average Filter, Gaussian Filter, and Wiener Filter were used for noise reduction, and then histogram equalization and adaptive thresholding were tested. For the second method, Gaussian Filter and Wavelet Transform were selected for noise reduction, and k-means clustering and morphological operations were applied to the images. In the segmentation phase performed for both methods, follicles were detected with the Canny Edge Detection algorithm. False Acceptance Rate (FAR) and False Rejection Rate (FRR) were used to evaluate the accuracy of the results. Our results show that the most accurate follicle detection was obtained by using the Wiener Filter and Gaussian Filter.

The aim of this study was to determine the best method for follicle detection using ovarian ultrasound images and to classify the ultrasound images as pcos or normal ovaries using the proposed CNN architecture. Two different methods for follicle detection have been proposed to evaluate pcos. For this purpose, the Median, the Mean, the Wiener, and the Gaussian filters were tested using standard and adaptive thresholds. Second, Gaussian filtering, Discrete Wavelet Transform, and k-means clustering algorithms were tested. The Canny operator separates follicles from the background in the segmentation phase. In this study, a CNN architecture that classifies limited ultrasound ovary images was developed, and its success in the best follicle detection method was presented. The highest follicle detection accuracy of 97.63% was achieved with adaptive thresholding using a Wiener filter. Besides, the ultrasound images of the ovaries were classified as "normal" or "polycystic ovary syndrome" using CNN architecture with classification accuracy of 65.81% for unsegmented ovarian images and 77.81% for segmented images. In addition to the proposed method, classification was performed using SqueezeNet-based transfer learning, which was successful in limited datasets, and 74.18% classification accuracy was achieved for the unsegmented images and 75.54 % for segmented images . The results show that the combination of the Wiener filter with adaptive thresholding was quite successful in follicle detection and that the CNN can better classify ovaries using preprocessed ultrasound images.