Development of Algorithm Tuberculosis Bacteria Identification Using Color Segmentation and Neural Networks

Abstract-- Tuberculosis (TB) is one of the primary cause of the death in the developing countries, certainly it is coming into lime light from various countries, either developed countries or developing countries. Sputum examination microscopically by using Ziehl-Neelsen stain (ZN-stain) method directly is a primary examination which is still used in all over the world included in Indonesia according to recommendation of the World Health Organization (WHO). Certainly this examination is depend on the expertise of the existing human resources and intensive examination time consuming. In the developing country with limited facility, a little number of expert, and not a cheap cost are some of the reason about how difficult to pressing the development of tubercular. Therefore, it is needed an automation in TB bacteria examination from a digital image of ZN-stain sample which can press the examination cost, time required, and human error. In this research, the algorithm of image processing for identification of TB bacteria is developed by using neural network. The testing result by using 15 hidden layer is obtained accuracy about 88%