Classification of Kidney Images Using Cuckoo Search Algorithm and Artificial Neural Network
S. M. K. Chaitanya1, P. Rajesh Kumar2
1S.m.k.chaitanya, Assistant Professor, Department of ECE, G.V.P. College of Engineering (Autonomous), Visakhapatnam (Andhra Pradesh), India.
2P.rajesh kumar, Professor, Department of ECE, Andhra University College of Engineering (Autonomous), Visakhapatnam (Andhra Pradesh), India.
Manuscript received on 18 February 2019 | Revised Manuscript received on 27 February 2019 | Manuscript published on 28 February 2019 | PP: 370-374 | Volume-8 Issue-3, February 2019 | Retrieval Number: C5953028319/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Ultrasound (US) imaging is used to provide the structural abnormalities like stones, infections and cysts for kidney diagnosis and also produces information about kidney functions. The goal of this work is to classify the kidney images using US according to relevant features selection. In this work, images of a kidney are classified as abnormal images by preprocessing (i.e. grey-scale conversion), generate region-ofinterest, extracting the features as multi-scale wavelet-based Gabor method, Cuckoo Search (CS) for optimization and Artificial Neural Network (ANN). The CS-ANN method is simulated on the platform of MATLAB and these results are evaluated and contrasted. The outcome of these results proved that the CS-ANNN had 100% specificity and 94% accuracy. By comparing it with the existing methods, the CS-ANN achieved 0% false-acceptance rate.
Keywords: Kidney Diagnosis, Gabor Feature Extraction, Cuckoo Search, Artificial Neural Network, Ultrasound Images.
Scope of the Article: Classification