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INTERNATIONAL JOURNAL OF CREATIVE RESEARCH THOUGHTS - IJCRT (IJCRT.ORG)
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ISSN Approved Journal No: 2320-2882 | Impact factor: 7.97 | ESTD Year: 2013
Call For Paper - Volume 12 | Issue 7 | Month- July 2024
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Paper Title
ULTRASONOGRAPHY KIDNEY IMAGE CLASSIFICATION BASED ON BACK-PROPAGATION NEURAL NETWORKS
Authors
Anisha.A.S, Dr.R.Kavitha Jaba Malar
Keywords
Classification, Noise, Pixel, Texture.
Abstract
Ultrasonography is one of the cheapest and protective approaches in medical imaging. This research aims at classification of medical ultrasound images of kidney as normal and abnormal kidney images In this work, the wiener filter is used to reduce the noise present in the image The gray-level co-occurrence matrix (GLCM) is used for examining the texture. It is used to find texture properties of an image by calculating the frequency of occurrence of pixel pairs with specific values and in a specified spatial relationship. Further, the Back-propagation Neural Network is used to classify the images as normal or abnormal kidney images. We got a better accuracy of 99%. The obtained result is then compared with the previous works.
IJCRT's Publication Details
Unique Identification Number - IJCRT1872300
Paper ID - 183847
Page Number(s) - 839-842
Pubished in - Volume 6 | Issue 1 | March 2018
DOI (Digital Object Identifier) - http://doi.one/10.1729/IJCRT.17413
Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Cite this article
Anisha.A.S, Dr.R.Kavitha Jaba Malar, "ULTRASONOGRAPHY KIDNEY IMAGE CLASSIFICATION BASED ON BACK-PROPAGATION NEURAL NETWORKS", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.6, Issue 1, pp.839-842, March 2018, Available at :http://www.ijcrt.org/papers/IJCRT1872300.pdf
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Volume 12 | Issue 7
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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