International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169 Volume: 11 Issue: 2s DOI: https://doi.org/10.17762/ijritcc.v11i2s.6087 Article Received: 28 November 2022 Revised: 26 December 2022 Accepted: 08 January 2023 ___________________________________________________________________________________________________________________ 230 IJRITCC | January 2023, Available @ http://www.ijritcc.org Machine Learning Approach for Comparative Analysis of De-Noising Techniques in Ultrasound Images of Ovarian Tumors Ms.Smital D. Patil 1 , Dr. Pramod J. Deore 2 1 Research Scholar, Department of Electronics and Telecommunication R. C. Patel Institute of Technology Shirpur, India smitalpatil55@gmail.com 2 Professor, Department of Electronics and Telecommunication R. C. Patel Institute of Technology Shirpur, India pjdeore@yahoo.com Abstract— Ovarian abnormalities such ovarian cysts, tumors, and polycystic ovaries are one of the serious disorders affecting women's health. In ultrasound imaging of ovarian abnormalities, noise during capturing of the image and its transmission process frequently corrupts the image. In order to make the best judgments possible at the appropriate moment, ovarian cysts in females must be accurately detected. In computer aided diagnosis of ovarian tumors, preprocessing is a very important step. In preprocessing, de-noising of medical images is a particularly a difficult task since it must be done while maintaining image features that are essential for diagnosis. In this research work we are using various denoising filters on ultrasound images of ovarian tumors. For different noise denoising techniques, performance measures like MSE, PSNR, SSIM, and UQI etc. are calculated. According to experimental findings, Block matching 3-D filter outperforms all other methods. Radiologists can better diagnose the condition with the use of this computer-assisted system. Keywords- Image de-noising, ovarian cysts, Machine learning, Block-matching, Polycystic ovaries. I. INTRODUCTION Ovaries are reproductive glands found only in females. An ovary is a reproductive gland that creates reproductive cells in female. These ovaries are held in place by a membrane on either side of the lower abdomen, adjacent to the uterus. The ovaries are an essential part of a woman’s reproductive system. They lie on each side of the uterus and produce estrogen and progesterone that are related to menstruation and pregnancy. An ovarian tumor is a cyst or an abnormal tissue growth (tumor) that may be non-cancerous i.e. benign, or malignant that develops in one or both female ovaries. •Ovarian cyst: A cyst is a tiny sac that contains liquid, air, or another substance •Ovarian Tumor: Any abnormal growth of additional tissue is referred to as a tumor. •Ovarian mass: A mass may be a cyst or a tumor growth that may be benign (non-cancerous) or malignant (cancerous). •PCO: Polycystic ovary (PCO) is a female endocrine disorder caused due to hormone imbalance that affects about 1 in 10 women. These are small follicular cysts. Ovarian abnormalities such ovarian cysts, tumors, and polycystic ovary are among the serious disorders affecting women's health (PCO). In order to make the best judgments possible at the appropriate moment, ovarian cysts in females must be accurately detected. Ovarian ultrasound imaging is an effective tool in infertility treatment as well as in ovarian cysts detection. Monitoring the follicles (poly-cystic ovaries) and ovarian tumors/cysts is especially important when women’s health is concerned. Radiologists manually observe and predict the presence of follicles or ovarian cysts. Figure 1: Female Reproductive System (everydayhealth.com) When an ultrasound image is acquired, transmitted, and retrieved, noise is always introduced. Speckle noise, AWGN (Additive White Gaussian Noise) and salt and pepper noise are