Abstract
Nowadays, medical image denoising is crucial for accurate diagnosis of the critical diseases. For denoising these images, conventional wavelet technique (universal threshold) uses a fixed value of threshold which is non-adaptive. The main aim of this paper is to develop a steepest descent (SD)-based learning algorithm, which is used in Artificial Neural Networks (ANN), to reduce the noise in images adaptively. A new soft thresholding function is proposed as the activation function of the ANN. From the results, it is found that proposed algorithm performed well when compared with conventional wavelet technique in terms of mean squared error (MSE), peak signal-to-noise ratio (PSNR).
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Rani, M.L.P., Sasibhushana Rao, G., Prabhakara Rao, B. (2019). ANN Application for Medical Image Denoising. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 816. Springer, Singapore. https://doi.org/10.1007/978-981-13-1592-3_53
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DOI: https://doi.org/10.1007/978-981-13-1592-3_53
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