An Efficient Denoising of Medical Images Through Convolutional Neural Network

Abstract

Denoising medical images is a critical step in enhancing image quality and improving diagnostic accuracy. In this work, an efficient denoising method has been proposed for medical images using convolutional denoising autoencoders. The proposed approach leverages the power of CNNs to learn complex patterns and features from a large dataset of clean and noisy medical images. To train the denoising network, a dataset has created consisting of pairs of clean medical images and their corresponding noisy versions. Various types and levels of noise are introduced to generate a diverse training set. The network architecture is carefully designed to effectively capture and extract relevant features from the noisy medical images. Multiple convolutional layers are used for feature extraction, followed by pooling, normalization, and non-linear activation layers. The final layers of the network focus on reconstructing the clean version of the input image. During the training phase, the network learns to map the noisy images to their corresponding clean versions. A suitable loss function, such as mean squared error or structural similarity index loss, is employed to guide the training process, and minimize the discrepancy between the network output and the ground truth clean image. The trained network is evaluated on a separate test dataset, and performance metrics such as peak signal-to-noise ratio and visual inspection are used to assess the denoising effectiveness. The experimental results demonstrate that the proposed CNN-based denoising method achieves superior performance compared to traditional denoising techniques. The network effectively reduces noise artifacts while preserving important image details and structures. The denoised medical images generated by the CNN can potentially lead to improved diagnosis and decision-making in medical applications.