Follicles Classification To Detect Polycystic Ovary Syndrome Using Glcm And Novel Hybrid Machine Learning

Abstract

A complex endocrine disorder is Polycystic ovary syndrome (PCOS). Women’s health is seriously affected by this. Female’s fertile nature is hindered by this ad many more issues are caused by this. Ultrasound can be used for detecting Polycystic Ovaries. Females should be aware of this disease to leads to a good life. In general, around 70% of this cases are undiagnosed. Earlier, adaptive k-means clustering technique is used for follicile segmentation with ultrasound images, but follicles abnormal and normal classification are not focused. So, for follicles abnormal and normal classification, an artificial neural network (ANN) technique with Improved Fruit Fly Optimization (IFFOA) is proposed in this work and named as (IFFOA-ANN) that avoids those risks. In this technique, for enhancing image quality, input ultrasound images are resized and noise in it are removed. Then, adaptive k-means clustering technique is used for processing follicles segmentation. In addition, statistical GLCM is used for introducing feature extraction model. At last, for classification, ANN will be trained using these features. With respect to accuracy, recall and precision, proposed model’s effectiveness is demonstrated in experimental results.