Checking radiological image is a very toilsome work for radiologists
because it requires long time practice and experienced skill. Therefore,
many computer-aided diagnosis (CAD) systems have been introduced
to cooperate with radiologists and nowadays many CAD systems based
on deep learning exceed human experts in diagnosing accuracy.
Nowadays, the much of progress has been made in designing
architectures. However, peculiar pre-processing method customized for
a certain problem can also increase the model accuracy. After checking
the LIDC dataset [44], it has been realized that the locations and sizes
of lungs were not regularized. Therefore, in this paper, a new pre-
processing method (lung-range-standardization) is proposed in order
to improve the general accuracy of lung-related diagnosis systems. And
the efficiency of the proposed pre-processing method is validated
through comparison between the nodule segmentation model trained
using our proposed pre-processing method and the nodule
segmentation model, which is trained using the prior pre-processing
methods. By using lung-range-standardization we could reduce the
difference between train loss and test loss in a great deal (from 0.337 to
0.119).
O Chung-Hyok, Ri Jong-Hyok, Om Chol-Nam
Kim Il Sung University, Democratic People’s Republic of Korea
Network Lung Nodule, Convolutional Neural Networks, Lung Cancer
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| Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 15 , Issue: 4 , Pages: 3630 - 3640 )
Date of Publication :
May 2025
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67
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