Optimization on Malaria Computer Aided Diagnostic System

Abstract

Malaria is infectious tropical disease found in tropical countries, caused by unicellular protozoan parasite. Microscopic based diagnosis of malaria is conducted by manually examining a thin blood smear that is acquired from the infected patients. This method requires a trained human interaction and therefore it is time consuming and prone to errors. Computer Aided Diagnostics (CAD) for Malaria was developed to speed up the diagnosis and maintaining the accuracy. The experimental results showed that the system is able to recognize the infected red blood cells, the species and the life phase of the infecting Plasmodium. However, the average runtime of the detection using the original version is 41.45 seconds per image, which is too long if it will be used in the field. By measuring the runtime of each process in the program, optimization can be done by re-writing or substituting the algorithm that causes the longest runtime. Four modifications are proposed: Connected Component Labelling with Bounding Box, Contour Tracing Labelling without Inner Contour Extraction, Downscaled Clump Splitting, and Concave Point Based Clump Splitting. The experimental results showed that the system has an average runtime of 1.73 seconds while maintaining the same level of accuracy compared to the original one.