Optimizing Medical Image Denoising Through Intelligent Recurrent Neural Networks with LSTM and Batch Normalization

Denoising medical images, often affected by random factors such as noise, time-varying parameters, and more, is a longstanding challenge in the domain of signal and image processing. Stochastic systems are also those influenced by random factors, which can be considered as random processes. Random processes are composed of a set of random variables. Each random variable at any given moment exhibits behavior that may follow a well-known probability distribution or an unknown distribution. Studying random processes assists us in gaining a better understanding of the behavior of our systems and enables us to make more accurate predictions about their future.In this comprehensive review article, we explore the remarkable advancements in the field of medical image denoising. Addressing the persistent challenge of noise in medical images, we introduce an effective denoising system that incorporates Long Short-Term Memory (LSTM), Batch Normalization, and Recurrent Neural Networks (RNN). We delve into the historical perspective, methodologies, applications, and comparative analysis of these techniques, highlighting their significant impact on medical imaging. Additionally, we discuss the relevance of stochastic systems and their role in understanding hidden dynamics within physical systems across various temporal and spatial scales. This review underscores the critical importance of these advancements in improving the quality and reliability of medical image analysis, facilitating more accurate diagnoses, and enhancing patient care.

Keywords:

Denoising , RNN , LSTM , Batch Normalization , Image processing