Title: A novel DeepCNN model for denoising analysis of MRI brain tumour images

Authors: B. Srinivas; Gottapu Sasibhushana Rao

Addresses: Department of ECE, MVGR College of Engineering (A), Vizianagaram-535005, Andhra Pradesh, India ' Department of ECE, Andhra University College of Engineering (A), Visakhapatnam-530003, Andhra Pradesh, India

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.

Keywords: DeepCNN; convolutional neural network; CNN; image denoising; deep denoiser; denoising CNN; DnCNN; general denoising filters; machine learning.

DOI: 10.1504/IJIIDS.2020.109462

International Journal of Intelligent Information and Database Systems, 2020 Vol.13 No.2/3/4, pp.393 - 410

Received: 22 Mar 2019
Accepted: 06 Jan 2020

Published online: 26 Aug 2020 *

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