Effect of data augmentation of renal lesion image by nine-layer convolutional neural network in kidney CT

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.