Open Access

ARTICLE

An Intelligent Classification System for Trophozoite Stages in Malaria Species

Siti Nurul Aqmariah Mohd Kanafiah^1,*, Mohd Yusoff Mashor¹, Zeehaida Mohamed², Yap Chun Way¹, Shazmin Aniza Abdul Shukor¹, Yessi Jusman³

1 Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, Arau, 02600, Malaysia
2 Department of Microbiology and Parasitology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, 16150, Malaysia
3 Department of Electrical Engineering, Faculty of Engineering, Universitas Muhammadiyah Yogyakarta, Yogyakarta, Indonesia

* Corresponding Author: Siti Nurul Aqmariah Mohd Kanafiah. Email:

Intelligent Automation & Soft Computing 2022, 34(1), 687-697. https://doi.org/10.32604/iasc.2022.024361

Received 14 October 2021; Accepted 29 November 2021; Issue published 15 April 2022

Abstract

Malaria is categorised as a dangerous disease that can cause fatal in many countries. Therefore, early detection of malaria is essential to get rapid treatment. The malaria detection process is usually carried out with a 100x magnification of thin blood smear using microscope observation. However, the microbiologist required a long time to identify malaria types before applying any proper treatment to the patient. It also has difficulty to differentiate the species in trophozoite stages because of similar characteristics between species. To overcome these problems, a computer-aided diagnosis system is proposed to classify trophozoite stages of Plasmodium Knowlesi (PK), Plasmodium Falciparum (PF) and Plasmodium Vivax (PV) as early species identification. The process begins with image acquisition, image processing and classification. The image processing involved contrast enhancement using histogram equalisation (HE), segmentation procedure using a combination of hue, saturation and value (HSV) color model, Otsu method and range of each red, green and blue (RGB) color selections, and feature extraction. The features consist of the size of infected red blood cell (RBC), brown pigment in the parasite, and texture using Gray Level Co-occurrence Matrix (GLCM) parts. Finally, the classification method using Multilayer Perceptron (MLP) trained by Bayesian Rules (BR) show the highest accuracy of 98.95%, rather than Levenberg Marquardt (LM) and Conjugate Gradient Backpropagation (CGP) training algorithms.

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Keywords

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