Machine Learning for Denoising Medical Images

Medical images are essential in diagnosing and monitoring various diseases. However, these images are often corrupted by noise, which can lead to inaccurate diagnosis and treatment decisions. To address this issue, machine learning techniques have been developed to denoise medical images effectively. In this paper, R. Rajeev explores the development and performance investigation of effective machine learning approaches for denoising medical images. The author focuses on various aspects of machine learning, such as artificial intelligence, deep learning, neural networks, convolutional neural networks, and autoencoders. Moreover, the author investigates supervised, unsupervised, and semi-supervised learning-based methods, as well as non-learning-based methods and filtering techniques, such as wavelet and Fourier transforms. The author also covers different types of noise that can corrupt medical images, including Gaussian, Poisson, speckle, and salt-and-pepper noise. The denoising techniques are applied to various types of medical images, including magnetic resonance imaging (MRI), computed tomography (CT), X-ray, ultrasound, nuclear medicine, positron emission tomography (PET), and single-photon emission computed tomography (SPECT), as well as digital pathology and histopathology. Furthermore, the author discusses other image processing techniques, such as image segmentation, registration, enhancement, and quality assessment. The denoised images are evaluated in terms of their diagnostic accuracy and clinical decision support, as well as their performance in image classification, retrieval, and interpretation. Overall, this paper provides a comprehensive overview of the current state-of-the-art in machine learning for denoising medical images, highlighting the potential for improving healthcare outcomes.