RADIAL BASIS FUNCTIONS BASED KIDNEY ABNORMALITY DETECTION AND CLASSIFICATION IN ULTRASOUND IMAGE

Authors

  • Anisha.A.S Assistant Professor, Department of Computer Science, Nanjil catholic college of arts and science, Kaliyakkavilai, India
  • Dr.R.Kavitha Jaba Malar Assistant Professor, Department of Computer Science, Nanjil catholic college of arts and science, Kaliyakkavilai, India

Keywords:

Abnormal, Feature Extraction, Pixel, Speckle noise, Texture

Abstract

Image processing is the advancement in the medical field. This research aims at classification of abdominal
ultrasound images of kidney as normal and abnormal kidney images. 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. In this research the Backpropagation Neural Network (BPNN) and Radial Basis Function (RBF) is used to classify the images as normal or
abnormal kidney images. We got a better accuracy of 99% for BPNN and 96% for RBF. The obtained result is then
compared and justified that the BPNN method is more efficient in the classification of US kidney Image.

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Published

2018-04-25

How to Cite

Anisha.A.S, & Dr.R.Kavitha Jaba Malar. (2018). RADIAL BASIS FUNCTIONS BASED KIDNEY ABNORMALITY DETECTION AND CLASSIFICATION IN ULTRASOUND IMAGE. International Journal of Advance Engineering and Research Development (IJAERD), 5(4), 1052–1056. Retrieved from https://www.ijaerd.org/index.php/IJAERD/article/view/3206

Issue

Vol. 5 No. 4 (2018): Volume 5 Issue 4 April 2018

Section

Articles