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Abstract: chronic kidney disease (CKD) is a widespread renal disease throughout the world. Once it develops to the advanced stage, serious complications and high risk of death will follow. Hence, early screening is crucial for the treatment of CKD. Since ultrasonography has no side effects and enables radiologists to dynamically observe the morphology and pathological features of the kidney, it is commonly used for kidney examination. In this study, we propose a novel convolutional neural network (CNN) framework named the texture branch network to screen CKD based on ultrasound images. This introduces a texture branch into a typical CNN to extract and optimize texture features. The model can automatically generate texture features and deep features from input images, and use the fused information as the basis of classification. Furthermore, we train the base part of the network by means of transfer learning, and conduct experiments on a dataset with 226 ultrasound images. Experimental results demonstrate the effectiveness of the proposed approach, achieving an accuracy of 96.01% and a sensitivity of 99.44%.

纹理分支网络：基于超声影像的慢性肾脏病筛查模型

[1]Acharya UR, Meiburger KM, Koh JEW, et al., 2019. Automated detection of chronic kidney disease using higher-order features and elongated quinary patterns from B-mode ultrasound images. Neur Comput Appl, in press.

[2]Ahmad M, Tundjungsari V, Widianti D, et al., 2017. Diagnostic decision support system of chronic kidney disease using support vector machine. Proc 2^nd Int Conf on Informatics and Computing, p.1-4.

[3]Batra A, Batra U, Singh V, 2016. A review to predictive methodology to diagnose chronic kidney disease. Proc 3^rd Int Conf on Computing for Sustainable Global Development, p.2760-2763.

[4]Chang EH, Chong WK, Kasoji SK, et al., 2017. Diagnostic accuracy of contrast-enhanced ultrasound for characterization of kidney lesions in patients with and without chronic kidney disease. BMC Nephrol, 18, Article 266.

[5]Charleonnan A, Fufaung T, Niyomwong T, et al., 2016. Predictive analytics for chronic kidney disease using machine learning techniques. Proc Management and Innovation Technology Int Conf, p.MIT-80-MIT-83.

[6]Chen H, Dou Q, Ni D, et al., 2015. Automatic fetal ultrasound standard plane detection using knowledge transferred recurrent neural networks. Proc 18^th Int Conf on Medical Image Computing and Computer-Assisted Intervention, p.507-514.

[7]Dalal N, Triggs B, 2005. Histograms of oriented gradients for human detection. Proc IEEE Computer Society Conf on Computer Vision and Pattern Recognition, p.886-893.

[8]Dhindsa K, Smail LC, McGrath M, et al., 2018. Grading prenatal hydronephrosis from ultrasound imaging using deep convolutional neural networks. Proc 15^th Conf on Computer and Robot Vision, p.80-87.

[9]Ecder T, 2013. Early diagnosis saves lives: focus on patients with chronic kidney disease. Kidney Int Suppl, 3(4):335-336.

[10]El Nahas AM, Bello AK, 2005. Chronic kidney disease: the global challenge. Lancet, 365(9456):331-340.

[11]Haralick RM, Shanmugam K, Dinstein I, 1973. Textural features for image classification. IEEE Trans Syst Man Cybern, SMC-3(6):610-621.

[12]He KM, Zhang XY, Ren SQ, et al., 2015. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. Proc IEEE Int Conf on Computer Vision, p.1026-1034.

[13]He KM, Zhang XY, Ren SQ, et al., 2016. Deep residual learning for image recognition. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.770-778.

[14]Ho CY, Pai TW, Peng YC, et al., 2012. Ultrasonography image analysis for detection and classification of chronic kidney disease. Proc 6^th Int Conf on Complex, Intelligent, and Software Intensive Systems, p.624-629.

[15]Hsieh JW, Lee CH, Chen YC, et al., 2014. Stage classification in chronic kidney disease by ultrasound image. Proc 29^th Int Conf on Image and Vision Computing New Zealand, p.271-276.

[16]Iqbal F, Pallewatte AS, Wansapura JP, 2017. Texture analysis of ultrasound images of chronic kidney disease. Proc 17^th Int Conf on Advances in ICT for Emerging Regions, p.1-5.

[17]Jeewantha RA, Halgamuge MN, Mohammad A, et al., 2017. Classification performance analysis in medical science: using kidney disease data. Proc Int Conf on Big Data Research, p.1-6.

[18]Kunwar V, Chandel K, Sabitha AS, et al., 2016. Chronic kidney disease analysis using data mining classification techniques. Proc 6^th Int Conf on Cloud System and Big Data Engineering, p.300-305.

[19]Levey AS, Eckardt KU, Tsukamoto Y, et al., 2005. Definition and classification of chronic kidney disease: a position statement from kidney disease: improving global outcomes (KDIGO). Kidney Int, 67(6):2089-2100.

[20]López V, Fernández A, García S, et al., 2013. An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics. Inform Sci, 250:113-141.

[21]Oquab M, Bottou L, Laptev I, et al., 2014. Learning and transferring mid-level image representations using convolutional neural networks. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.1717-1724.

[22]Pujari RM, Hajare VD, 2014. Analysis of ultrasound images for identification of chronic kidney disease stages. Proc 1^st Int Conf on Networks & Soft Computing, p.380-383.

[23]Sharma K, Virmani J, 2016. Classification of renal diseases using first order and higher order statistics. Proc 3^rd Int Conf on Computing for Sustainable Global Development, p.425-430.

[24]Shen W, Zhou M, Yang F, et al., 2015. Multi-scale convolutional neural networks for lung nodule classification. Proc 24^th Int Conf on Information Processing in Medical Imaging, p.588-599.

[25]Shin HC, Roth HR, Gao MC, et al., 2016. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imag, 35(5):1285-1298.

[26]Subramanya MB, Kumar V, Mukherjee S, et al., 2015. SVM-based CAC system for B-mode kidney ultrasound images. J Dig Imag, 28(4):448-458.

[27]Telea A, 2004. An image inpainting technique based on the fast marching method. J Dig Imag, 9(1):23-34.

[28]Zheng Q, Furth SL, Tasian GE, et al., 2019. Computer-aided diagnosis of congenital abnormalities of the kidney and urinary tract in children based on ultrasound imaging data by integrating texture image features and deep transfer learning image features. J Pediatr Urol, 15(1):75.e1-75.e7.