Machine Learning Approach for Comparative Analysis of De-Noising Techniques in Ultrasound Images of Ovarian Tumors

Ovarian abnormalities such ovarian cysts, tumors, and polycystic ovaries are one of the serious disorders affecting women's health. In ultrasound imaging abnormalities, noise during capturing image its transmission process frequently corrupts image. order to make best judgments possible at appropriate moment, cysts in females must be accurately detected. computer aided diagnosis preprocessing is a very important step. preprocessing, de-noising medical images particularly difficult task since it done while maintaining features that essential for diagnosis. this research work we using various denoising filters on tumors. For different techniques, performance measures like MSE, PSNR, SSIM, UQI etc. calculated. According experimental findings, Block matching 3-D filter outperforms all other methods. Radiologists can better diagnose condition with use computer-assisted system.