Application Of Neural Network In Malaria Parasites Classification - Lim, Chia Li (Universiti Sains Malaysia, 2006)
There are only a few researchers used artificial intelligence to classify malaria parasites. The purpose of this project is to classify malaria parasites into Plasmodium falciparum, Plasmodium vivax and Plasmodium malariae based on ratio of infected red blood cell’s (RBC) size to normal RBC’s size, shape of parasite, location of chromatin, number of chromatin, texture of infected RBC, and number of parasite per RBC using different types of neural network. Throughout the project, the suitability of the application of neural networks in malaria parasites classification will be investigated. The best neural network will be implemented to build an intelligent classifier for malaria parasites. The first stage of this project is to develop the neural network using MATLAB Neural Network Toolbox and Borland C++ Builder. Multilayer Perceptron (MLP) network and Radial Basis Function (RBF) network will be developed using MATLAB in which MLP network is trained with Back Propagation, Bayesian Rule and Levenberg-Marquardt learning algorithm and RBF network is trained with k-means clustering algorithm. Hybrid Multilayer Perceptron (HMLP) network with modified recursive prediction error algorithm will be developed using Borland C++ Builder. In the second stage, comparison will be done on the performance of neural networks developed to yield the best neural network and malaria parasites classification system will be developed using Borland C++ Builder. Result shows that HMLP network is the best neural network in classification of malaria parasites. It has a simple architecture, intelligent and accurate. The final product of this project is a software system that is capable to classify malaria parasites with high accuracy, high applicability, fast and cheap.