01612nas a2200253   4500000000100000000000100001008004100002260001200043653001100055653001500066653000900081653001300090653002200103100002400125700001800149700001900167700001800186245008300204856009900287300001000386490000600396520094200402022001401344       2017                            d  c09/201710aKmeans10aClustering10aTest10aMedicine10aFeature Selection1 aVishwanath Bijalwan1 aMeenu Balodhi1 aPragya Bagwari1 aBhavya Saxena00aComparison of Feedforward Network and Radial Basis Function to Detect Leukemia  uhttp://www.ijimai.org/journal/sites/default/files/files/2016/12/ijimai20174_5_10_pdf_10782.pdf  a55-570 v43 aLeukemia is a fast growing cancer also called as blood cancer. It normally originates near bone marrow. The need for automatic leukemia detection system rises ever since the existing working methods include labor-intensive inspection of the blood marking as the initial step in the direction of diagnosis. This is very time consuming and also the correctness of the technique rest on the worker’s capability. This paper describes few image segmentation and feature extraction methods used for leukemia detection. Analyzing through images is very important as from images; diseases can be detected and diagnosed at earlier stage. From there, further actions like controlling, monitoring and prevention of diseases can be done. Images are used as they are cheap and do not require expensive testing and lab equipment. The system will focus on white blood cells disease, leukemia. Changes in features will be used as a classifier input.
  a1989-1660