Machine Learning Approaches for Image Denoising and Artifact Removal in Medical Imaging
Keywords:
Recurrent Neural Network (RNN), CT lung images, Long Short-Term Memory (LSTM)Abstract
This study introduces a novel Recurrent Neural Network (RNN) for medical picture denoising that makes use of lengthy short-term memory-based batch normalisation. At first, noisy CT lung pictures are used as input. Denoising the input picture using an RNN. The training of deep neural networks that are feed-forward may now be sped up with the use of a technique known as batch normalisation. Batch normalisation is used in long-term short-term memory (LSTM), and it is shown to speed up optimisation and enhance generalisation. The Particle Swarm Optimisation (PSO) technique is used to choose the batch size in batch normalisation. MATLAB was used to create the suggested system. The experimental findings are compared to the current setup.
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