An automated cells counting system for Malaria based on thick blood smear samples

Malaria is one of the most serious blood infection disease that remains to be a global public health challenge especially in African region. An estimated total of 241 million of malaria cases happened worldwide which leads to 627 000 deaths in 2020, found on statistic reported by World Health Organization (WHO). Malaria is caused by plasmodium parasites carried through contaminated female Anopheles mosquito. There are five types of plasmodium parasites, yet Plasmodium Falciparum and Plasmodium Vivax are the main species that most commonly detected worldwide. Based on the high number of malaria occurrence, it is crucial to do medical inspection every year. Currently, microscopy test using thick blood smear still be the standard method for malaria detection. However, this procedure is time consuming and prone to human error. Nowadays, image processing is recognized as a quick ways to analyze a lot of blood samples. Therefore, there is an urge to develop an automated cells counting system for malaria based on thick blood smear samples. Thus, this research has established an automated cells counting system for malaria detection by using several image processing such as image enhancement, image segmentation and intelligent classifiers. Starting with image enhancement, there are various contrast enhancement techniques and colour constancy techniques that were applied to enhance the malaria images. Next, the malaria images was segmented by using several thresholding and clustering techniques. Here, phansalkar technique able to achieve good segmentation performance in terms of accuracy, specificity and sensitivity with value of 99.86%, 99.87% and 92.47%, respectively. In addition, phansalkar technique able to segment malaria parasites as well as obtain the fully segmented malaria parasite region with clean segmented malaria images. After that, size, shape, texture and colour based features were extracted from the segmented parasite to be used as inputs to the three different types of classifiers namely multi-layered perceptron (MLP) trained by Levenberg-Marquardt (LM), single-hidden layer feed forward neural network (SLFN) trained by extreme learning machine (ELM) and online sequential extreme learning machine (OS-ELM). Overall, the automated cells counting system for malaria that has been developed using MLP network trained by LM algorithm is capable to perform the classification between malaria and non-malaria parasites by utilizing a total of 7500 segmented cells extracted from 300 malaria images with validation accuracy of 86.78%. To conclude, the proposed automated cells counting system for malaria based on thick blood smear samples is capable to perform the detection of malaria parasites using thick blood smear images by producing high counting accuracy with total counting of 1736 parasites from 300 total images and achieve accuracy of 86.78%.