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

B. Srinivas, G. Rao
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引用次数: 1

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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一种新的用于MRI脑肿瘤图像去噪分析的DeepCNN模型
必须向专家或医生介绍医学图像,以高精度地诊断脑肿瘤等重大疾病。本文提出了一种新的DeepCNN模型来执行MRI脑肿瘤图像去噪任务,并将结果与预训练DnCNN、高斯滤波器、自适应滤波器、双边滤波器和引导滤波器进行了比较。研究发现,DeepCNN的性能优于其他过滤方法。利用5 ~ 50级的噪声和椒盐噪声、泊松噪声、高斯噪声、散斑噪声等构成噪声图像。计算并比较了所有滤波器和噪声的峰值信噪比和结构相似性指数等性能指标。所提出的DeepCNN模型对于未知和已知噪声水平的去噪都有很好的效果。由于使用了17个卷积层和批处理归一化,它加快了训练过程,也提高了去噪性能。
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来源期刊
CiteScore
2.90
自引率
0.00%
发文量
21
期刊介绍: Intelligent information systems and intelligent database systems are a very dynamically developing field in computer sciences. IJIIDS provides a medium for exchanging scientific research and technological achievements accomplished by the international community. It focuses on research in applications of advanced intelligent technologies for data storing and processing in a wide-ranging context. The issues addressed by IJIIDS involve solutions of real-life problems, in which it is necessary to apply intelligent technologies for achieving effective results. The emphasis of the reported work is on new and original research and technological developments rather than reports on the application of existing technology to different sets of data.
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