2020 RISP International Workshop on Nonlinear Circuits,Communications and Signal Processing (NCSP 2020)
Honolulu, Hawaii, USA, February 28 - March 2, 2020

Comparison between Denoising Convolutional Neural Networks
and Simple Convolutional Neural Networks
for Denoising of Medical Images
Aika Ohno, Yuichi Miyata, Yoko Uwate, and Yoshifumi Nishio
Dept. of Electrical and Electronic Engineering, Tokushima University
2-1 Minami-Josanjima, Tokushima 770-8506, Japan
Email: {a-ohno, y.miyata, uwate, nishio}@ee.tokushima-u.ac.jp

Abstract
In this study, we compare between Denoising Convolutional
Neural Networks (DnCNN) and simple Convolutional NeuralNetworks (CNN). We apply these networks for small datasetof chest radiographs. Then, we evaluate them by visual observation as qualitative evaluation and Peak Signal to Noise
Ratio (PSNR) as quantitative evaluation.

Input

Convolutional Layer 1

1. Introduction
Convolutional Neural Networks (CNN) are one method ofConvolutional Layer 2
Artificial Intelligence (AI), and CNN has enormous attention
in recent years. Nowadays, it is used for various kinds offields and products. Then, CNN is applied for medical imOutputages as well. For accurate diagnosis it is important to havegood quality of medical images. However, medical imaging
systems have trade-off relationships between filming time, radiation dose and quality of images. It is also important for patients to have less filming time and radiation dose. Therefore,
noise would be generated in medical images.
Recently, CNN for denoising of medical images has
Figure 1: Structure of simple CNN.
been discussed. Denoising Convolutional Neural Networks(DnCNN) is one of the state-of-the-art denosing Networks[1].
In this study, we compare between DnCNN and simple
CNN for denoising Gaussian noise on Chest Radiographs.
Then, denoising by simple CNN showed better results thanDnCNN. The result images of denoising by DnCNN had 3. Denoising Convolutional Neural Networks
white spots that are not expected.
2. Convolutional Neural Networks

DnCNN can be described one of the state-of-the-art denosing Networks. It has Convolution layer and repetition of Convolution and Batch Normalization layers.

CNN is one of the most popular Neural Networks, and it isused for various studies.
In this study, we use simple CNN which has only two ConIn this study, Convolutional and Batch Normalization layvolutional layers. Kernel size is 3, batch size is 4, and activa- ers are repeated 16 times. Kernel size is 3, batch size is 4, andtion function is ReLU. Figure 1 shows the structure of simple Activation function is ReLU. Figure 2 shows the structure ofCNN that we use.
simple DnCNN that we use.

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6. Results

Input

Convolution

First, we show the graphs about loss function. In this study,
we use mean square error as loss function.
Convolution + Batch Normalization

×16……..
Convolution

OutputFigure 2: Structure of simple DnCNN.
4. Dataset
We use Japanese Society of Radiological Technology(JSRT) Database [2]. In this dataset, there are both of withand without nodule images. From this database, we use 200images. The ratio of train data and test data is 3:1. Test data
is used for validation as well. Image size is 256 × 256 pixels,and images are gray scale. The table2 shows the number ofimages with and without nodule in the dataset.
Table 1: Dataset.
train data
With nodule images
94
Without nodule images
56
Total
150

Figure 3: Loss of DnCNN.

test data
311950Then, we make noisy images by putting Gaussian noiseon the dataset. Gaussian noise is one of the most common
kinds of noise for medical images. We put Gaussian noise ofstandard deviation 50 and mean 0.
5. Peak Signal To Noise Ratio
We use Peak Signal to Noise Ratio (PSNR) as quantitative evaluation. PSNR can express degradation of images,andlarge number of PSNR means less degradation. In this study,
PSNR is defined by the following equation [3].
P SN R = 10 log10

M AX 2
M SE(1)

MAX is the maximum pixel that the images can be. MSE ismean square error between original image and result image.

Figure 4: Loss of simple CNN.

The loss of train of DnCNN does not converge, and the lossof validation of DnCNN does not converge at all [Figure 3].
On the other hand, the loss of CNN converges about both oftrain data and validation data [Figure 4].

110Figure 7: Original Image.
Figure 5: PSNR of DnCNN.

Figure 8: Noise image.
Figure 6: PSNR of simple CNN.
PSNR of DnCNN is not stable at all [Figure 5]. The highest
PSNR of train data is 29.3015 and the highest PSNR of validation data is 28.6190. PSNR of CNN converges, and PSNRof train data indicates 29.8867 and PSNR of validation dataindicates 29.8139 at 50th epochs [Figure 6]. Even the highest
PSNRs of DnCNN are lower than PSNRs of CNN. Then, weshow the processing time result compared between DnCNNand CNN [Table2].

Table 2: Processing Time.
DnCNN
2h47m
CNN
15m25sWe use GPU for processing both of DnCNN and CNN.
Processing time of CNN is dramatically shorter than DnCNN.

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Figure 9: Denoising result image by simple CNN.

Figure 10: Denoising result image by DnCNN.
As we can compare between Figure 7, 8, and 10, about
denoising result images by DnCNN we can see white spotsthat do not exist on original images. That white spots appear
mainly background and the black space in lungs. Especially,
white spots in lungs would be problems for diagnosis. On
the other hand, result images by simple CNN have less whitespots than DnCNN [Figure 9]. Even though we can still see
Gaussian noises on result images and blurred noise in lungs
aw well, denoising result images by simple CNN are better
than DnCNN ones.
7. Conclusion
We could reduce Gaussian noise from noisy Chest Radiographs by using simple CNN. Simple CNN showed better results than DnCNN with small size of dataset. Moreover, Simple CNN processed rapidly compared with DnCNN. However, noise reduction is not enough yet. In the future, we willconsider better network structures for denoising.
References[1] Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng,and Lei Zhang,”Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising”, IEEETransactions on Image Processing, July 2017.
[2] Shiraishi J, Katsuragawa S, Ikezoe J, Matsumoto T,
Kobayashi T, Komatsu K, Matsui M, Fujita H, Kodera
Y, and Doi K, ”Development of a digital image databasefor chest radiographs with and without a lung nodule:
Receiver operating characteristic analysis of radiologists detection of pulmonary nodules” AJR 174, 71-74,
2000.
[3] Hiroshi Fujita,”Medical AI and Deep Learning Series
No.3”, Ohmsha, 2019.

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