Abstract
Medical images must be introduced to the specialists or doctors with high accuracy for the diagnosis of critical diseases like a brain tumour. In this paper, a novel DeepCNN model is proposed to perform MRI brain tumour image denoising task and the results are compared with pre-trained DnCNN, Gaussian, adaptive, bilateral and guided filters. It is found that DeepCNN performs better than other filtering methods used. Different noise levels ranging from 5 to 50 and noises like salt and pepper, Poisson, Gaussian, and speckle noises are used to form the noisy images. Performance metrics like peak signal to noise ratio and structural similarity index are calculated and compared across all filters and noises. The proposed DeepCNN model performs well for denoising with the unknown and known noise levels. It speeds up the training process and also improves the denoising performance because of using 17 convolutional layers and batch normalisation.
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