RADIAL BASIS FUNCTIONS BASED KIDNEY ABNORMALITY DETECTION AND CLASSIFICATION IN ULTRASOUND IMAGE
| Author(s) | : | Anisha.A.S, Dr.R.Kavitha Jaba Malar |
| Institution | : | Assistant Professor, Department of Computer Science, Nanjil catholic college of arts and science, Kaliyakkavilai, India |
| Published In | : | Vol. 5, Issue 4 — April 2018 |
| Page No. | : | 1052-1056 |
| Domain | : | Engineering |
| Type | : | Research Paper |
| ISSN (Online) | : | 2348-4470 |
| ISSN (Print) | : | 2348-6406 |
Image processing is the advancement in the medical field. This research aims at classification of abdominalultrasound images of kidney as normal and abnormal kidney images. The wiener filter is used to reduce the noise presentin 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 orabnormal kidney images. We got a better accuracy of 99% for BPNN and 96% for RBF. The obtained result is thencompared and justified that the BPNN method is more efficient in the classification of US kidney Image.
Anisha.A.S, Dr.R.Kavitha Jaba Malar, “RADIAL BASIS FUNCTIONS BASED KIDNEY ABNORMALITY DETECTION AND CLASSIFICATION IN ULTRASOUND IMAGE”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 5, Issue 4, pp. 1052-1056, April 2018.
