@Article{cmes.2020.010753,
AUTHOR = {Liying Wang , Zhiqiang Xu, Shuihua Wang},
TITLE = {Effect of Data Augmentation of Renal Lesion Image by Nine-layer Convolutional Neural Network in Kidney CT},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {124},
YEAR = {2020},
NUMBER = {3},
PAGES = {1001--1015},
URL = {http://www.techscience.com/CMES/v124n3/39924},
ISSN = {1526-1506},
ABSTRACT = {Artificial Intelligence (AI) becomes one hotspot in the field of the medical images analysis and provides rather promising solution. Although some
research has been explored in smart diagnosis for the common diseases of urinary
system, some problems remain unsolved completely A nine-layer Convolutional
Neural Network (CNN) is proposed in this paper to classify the renal Computed
Tomography (CT) images. Four group of comparative experiments prove the
structure of this CNN is optimal and can achieve good performance with average
accuracy about 92.07 ± 1.67%. Although our renal CT data is not very large, we
do augment the training data by affine, translating, rotating and scaling geometric
transformation and gamma, noise transformation in color space. Experimental
results validate the Data Augmentation (DA) on training data can improve the performance of our proposed CNN compared to without DA with the average accuracy about 0.85%. This proposed algorithm gives a promising solution to help
clinical doctors automatically recognize the abnormal images faster than manual
judgment and more accurately than previous methods.},
DOI = {10.32604/cmes.2020.010753}
}