An Efficient Optimized Probabilistic Neural Network Based Kidney Stone Detection and Segmentation over Ultrasound Images
Raju.P1, Malleswara Rao.V2, Prabhakara Rao.B3
1Raju.P, Research scholar, Dept of ECE, JNTU Kakinada, Andhra Pradesh, India.
2MalleswaraRao.V, Professor, Dept of ECE, GITAM, Visakhapatnam, Andhra Pradesh, India.
3PrabhakaraRao.B, Program Director, dept Nanotechnology, JNTU Kakinada, Andhra Pradesh, India.
Manuscript received on 02 August 2019. | Revised Manuscript received on 07 August 2019. | Manuscript published on 30 September 2019. | PP: 7465-7473 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5677098319/2019©BEIESP | DOI: 10.35940/ijrte.C5677.098319
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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: Locating renal calculus in the ultrasound image is a demanding requirement in the field of medical imaging. For accurate detection of kidney stone, in this paper, optimal recurrent neural network (OPNN) is adopted. The proposed work undergoes pre-processing, feature extraction, classification, and segmentation. Initially, the noise present in input images is removed with the median filter because noises impact the accuracy of the classification. Then, compute features of this image. In the classification stage, features are used to classify defects through optimal probabilistic NeuralNetwork (OPNN). OPNN is a combination of PNN and spider monkey optimization (SMO). The parameter of PNN is optimized with the help of SMO. Then, the stone region from the abnormal image is segmented using probabilistic fuzzy c-means clustering (PFCM). The proposed methodology performance can be analyzed by using Sensitivity, Accuracy, and Specificity.
Keywords: Kidney Stone, Optimal Recurrent Neural Network, Feature Extraction, Spider Monkey Optimization, Probabilistic Fuzzy C-means Clustering.
Scope of the Article: Fuzzy Logics