Image Denoising For Medical Image Analysis

Abstract

The aim of this work is to identify the optimal Machine Learning (ML) techniques for image denoising in radiological medical applications.   MRI diagnosis of brain tumors, X-ray analysis of the chest, MRI imaging for breast cancer, US Computer Aided Diagnosis (CAD) and detection of skin and breast abnormalities, and Medical Ultrasound (US) for prenatal development are the six specific radiology areas examined in the examination.   The machine learning methods that demonstrate remarkable accuracy in radiologists' medical diagnoses are the main topic of this report.   Among the picture denoising techniques discussed are curvelet algorithms, wavelet-based medical denoising, basic filtering techniques, and optimization strategies. Often, machine learning outperforms traditional picture denoising techniques.   To get fast and efficient results, radiologists are increasingly applying machine learning techniques to MRI, US, X-ray, and skin lesion images.   The paper also discusses the challenges researchers have when applying machine learning and image denoising techniques in clinical contexts, as well as the features and contributions of various machine learning methodologies.