Classification of Kidney Images using Particle Swarm Optimization Algorithm and Artificial Neural Networks
S. M. K. Chaitanya1, P. Rajesh Kumar2
1S.M.K. Chaitanya, Assistant Professor, Department of ECE, GVP 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 05 February 2019 | Revised Manuscript received on 13 February 2019 | Manuscript published on 28 February 2019 | PP: 526-530 | Volume-8 Issue-4, February 2019 | Retrieval Number: D2800028419/19©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 able to produce information about kidney functions. The main aim of this work is classifying the kidney images by using US according to relevant features selection. In this work, images of kidney are classified as abnormal images by pre-processing (i.e. grey-scale conversion), generate region-of-interest, extracting the features as multi-scale wavelet-based Gabor method, Particle Swarm algorithm (PSO) for optimization and Artificial Neural Networks (ANN). The PSO-ANN method is simulated on the platform of MATLAB and these results are evaluated and contrasted. The results obtained through this method are better in terms of accuracy, sensitivity and specificity.
Keyword: Artificial Neural Networks, Gabor Feature Extraction, Kidney Diagnosis, Particle Swarm Optimization, Ultrasound Images.
Scope of the Article: Classification