Female Cancer Control Diagnosis by Decision Making using Image Processing - A Prospective Study
Abstract: In this article, an effective method is implemented for the feature extraction of renal tumor. Image segmentation is vital in many medical image diagnostic applications. It identifies the region of interest in a data. The outline of semi - programmed division contains the following stages, (i) Initial one is the Region of Renal interest (ROI), (ii) the Second one is automatize iterative method. (iii) the third is a novel work to select the best classifier for female optimal features. This is an optimal method to shun the CT guided Biopsy. An optimum method is chosen for overcoming the limitations seen in the morphological operation method and getting the degree of correspondence. Boundary segmentation of renal tumor extends to feature extraction of images and classification. The high specificity of Fuzzy is 99.5 % for the left and 99.4 % for the right tumor. The qualitative examination done on 37 out of 67 subjects indicates the average comparison of accuracy 95.7 % and 94.7 % (Left and Right region) respectively between the successive classifiers.
Keywords: Renal, Tumor, Active contour method, segmentation, Boundary Detection, Feature Extraction, Classifier,
How to Cite?: R Gomalavalli, Dr. P. M. Venkata Sai, Dr. K. Sriram, Dr. S. Muttan, "Female Cancer Control Diagnosis by Decision Making using Image Processing - A Prospective Study", Volume 7 Issue 1, January 2018, International Journal of Science and Research (IJSR), Pages: 1320-1328, https://www.ijsr.net/getabstract.php?paperid=ART20179632, DOI: https://dx.doi.org/10.21275/ART20179632