Counting and classification of white blood cell using Artificial Neural Network (ANN)
Shubham Manik, Lalit Mohan Saini, Nikhil Vadera · 2016
Quantitative microscopy has improved conventional diagnostic method through better comprehension of microscopic features from a clinical point of view. The extraction of the nucleus from the blood smear images of white blood cells (WBC) gives the profitable data to specialists for classification of various types of disease as the vast majority of the illnesses present in the body can be distinguished by investigating blood. It is extremely time-consuming and tedious to segment the nucleus manually and also classification which is done on the premise of the instruments which are utilized by specialists for segmentation and classification of white blood cells are not economical for every doctor or hospital; so proposed method is ideal which decreases the execution time of segmentation and classification. In this paper; a new framework is proposed to enhance detection and classification of Leukocytes i.e. Nucleus Enhancement by finding Intensity maxima and then classified on the basis of various features extracted from segmented images. Classification is done by Artificial Neural Network (ANN).