Detection and Classification of Kidney Disorders using Deep Learning Method


Vasanthselvakumar R,Balasubramanian M,Palanivel S,



Adaboost,Chronic Kidney Diseases, HOG,Convolutional Neural Network,Ultrasound image,


The main objective of this work is to detect and classify the chronic kidney diseases (CKDs) particularly kidney stone, cystic kidney and suspected renal carcinoma. CKDs make a ground for developing several numbers of diseases other than urinal system. It will cause the pervasiveness of Coronary heart diseases, stroke, cardiomyopathy, pulmonary hypertension, and heart valves diseases, Early prediction of chronic kidney disease will save life from worse diseases, Ultrasound imaging is widely used diagnostic method for abdominal studies. In this proposed system chronic kidney diseases have detected using a framework containing Histogram of oriented gradient feature and Adaboost Algorithm. Convolution Neural Network (CNN) multi layered architecture has trained for kidney diseases classification, Batch prediction method is evaluated for prediction of chronic kidney diseases. The performance accuracy for detection of kidney disease is given as 96.67% The accuracy for the classification of CKD ultrasound using CNN is given by 85.2 %..


I.Atsushi Takemura, Akinobu Shimizu, and Kazuhiko Hamamoto, “Discrimination of Breast Tumors in Ultrasonic Images Using an Ensemble Classifier Based on the AdaBoost Algorithm With Feature Selection”, IEEE transactions on Medical Imaging, Vol. 29, no. 3, pp 598-609, March 2010.

II.Chensi Cao, Feng Liu, Hai Tan, Deshou Song, Wenjie Shu, WeizhongLi, Yiming Zhou, Xiaochen Bo, ZhiXie “Deep Learning and Its Applications in Biomedicine”, Elsevier Transaction on Genomics Proteomics Bioinformatics, vol. 16, pp. 17-32, Mar 2018.

III.Fangwang, KevinHe, JinweiWang, MingHuiZhao, YiLiLuxiaZhang, RajivSaran, Jennifer L.Bragg Gresham, “Prevalence and Risk Factors for CKD: A Comparison Between the Adult Populations in China and the United States” Elsevier transaction on Kidney International Reports, vol. 3, No. 5, pp. 1135-1143 Sep 2018.

IV.Hidenori Ide Takio Kurita, “Improvement of Learning for CNN with ReLU Activation by Sparse Regularization”, In Proc. International Joint Conference on Neural Networks (IJCNN) pp. 2684-2691. Jul 2017.

V.Hyunho Choi, JechangJeong ” Speckle Noise Reduction in Ultrasound Images using SRADand Guided Filter”, In proc IEEE International Workshop on Advanced Image Technology (IWAIT) , pp no 1-4, Jan 2018

VI.Kemal Adem, SerhatKiliçarslan, OnurCömert, “Classification and diagnosis of cervical cancer with softmax classification with stacked autoencoder” Elsevier transaction on Expert Systems With Applications Vol 115, pp 557-564, Jan 2019.

VII.Ling Zhang , Le Lu, Isabella Nogues, Ronald M. Summers, Shaoxiong Liu, and Jianhua Yao.” DeepPap: Deep Convolutional Networks for Cervical Cell Classification”, IEEE transaction on Journal of Biomedical and Health Informatics, vol. 21, no. 6, Nov 2017.

VIII.M. Balasubramanian, S. Palanivel, V. Ramalingam, “Video-based person recognition using fovea intensity comparison code”, Behaviour & Information Technology, November Vol.30, No. 6, pp. 747-760. 2011.,

IX.Matthieu de La Roche Saint Andre, Laura Rieger, Morten Hannemose, and Junmo Kim, “Tunnel Effect in CNNs: Image Reconstruction from Max Switch Locations”, IEEE Signal Processing Letters, Vol. 24, No. 3, pp. Mar 2017.

X.Mr.B.Perumal, 2 Dr.M.PallikondaRajasekaran, “A Hybrid Discrete Wavelet Transform with Neural Network Back Propagation Approach for Efficient medical Image Compression”, In Proc International Conference on Emerging Trends in Engineering, Technology and Science(ICETETS), pp 1-5, Feb 2016.

XI.Pietro Perona, Jitendra Malik, “Scale-Space and Edge Detection Using Anisotropic Diffusion”, IEEE Transactions ON Pattern Analysis and Machine Intelligence, vol. 12. no. 7, Pp 629-639, July 1990

XII.R. Vasanthselvakumar, M. Balasubramanian, S.Palanivel, “Pattern Analysis of Kidney Diseases For Detection And Classification Using Ultrasound B -Mode Images”, International Journal of Pure and Applied Mathematics, Volume 117 No. 15, pp. 635-653, 2017.

XIII.Yanwei Pang, Manli Sun, XiaohengJiang, and Xuelong Li, “Convolution in Convolution for Network in Network” IEEE Transaction on Neural Networks and Learning Systems, Vol. 29, no. 5, pp. 1587-1597, May 2018.

XIV.Yongjin Zhou, Jingxu Xu, Qiegen Liu, Cheng Li, Zaiyi Liu, Meiyun Wang, Hairong Zheng, and Shanshan Wang, “A Radiomics Approach With CNN forShear-Wave Elastography Breast Tumor Classification”, IEEE Transactions on Biomedical Engineering, vol. 65, no. 9, pp. 1935-1942, Sep 2018.

XV.YujiIwahoria, AkiraHattoria, YoshinoriAdachia, M.K.Bhuyanb, Robert J. Woodhamc, KunioKasugaid, “Automatic Detection of Polyp Using Hessian Filter and HOG Features”, Elsevier transaction on Procedia of Computer Science, Volume 60, pp. 730-739, 2015

Vasanthselvakumar R, Balasubramanian M, Palanivel S View Download