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Reseach Article

Comparative Analysis of different types of Malaria Diseases using First Order Features

by Ajala,Funmilola. A, Fenwa,Olusayo. D, Aku,Micheal. A.
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 8 - Number 3
Year of Publication: 2015
Authors: Ajala,Funmilola. A, Fenwa,Olusayo. D, Aku,Micheal. A.

Ajala,Funmilola. A, Fenwa,Olusayo. D, Aku,Micheal. A. . Comparative Analysis of different types of Malaria Diseases using First Order Features. International Journal of Applied Information Systems. 8, 3 ( February 2015), 20-26. DOI=10.5120/ijais15-451297

@article{ 10.5120/ijais15-451297,
author = { Ajala,Funmilola. A, Fenwa,Olusayo. D, Aku,Micheal. A. },
title = { Comparative Analysis of different types of Malaria Diseases using First Order Features },
journal = { International Journal of Applied Information Systems },
issue_date = { February 2015 },
volume = { 8 },
number = { 3 },
month = { February },
year = { 2015 },
issn = { 2249-0868 },
pages = { 20-26 },
numpages = {9},
url = { },
doi = { 10.5120/ijais15-451297 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2023-07-05T18:58:49.513221+05:30
%A Ajala,Funmilola. A
%A Fenwa,Olusayo. D
%A Aku,Micheal. A.
%T Comparative Analysis of different types of Malaria Diseases using First Order Features
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 8
%N 3
%P 20-26
%D 2015
%I Foundation of Computer Science (FCS), NY, USA

Malaria is majorly caused by three parasitic organisms namely, P. malaria, P. vivax, P. falciparum. Physician (Micro-biologists, Laboratory technicians, Medical Practitioners, and medical experts) examines erythrocytes under light microscope to study the color and morphological changes toward malaria diagnosis. Assessment accuracy depends on the physician-pathological understanding. To help physicians in cases where they may be wrong, developing a computer assisted malaria parasite detection tool has helped modern pathological services where a physician is able to get assistance in order to quickly make better decision toward malaria diagnosis. Segmentation of medical images helps in identifying features that are needed for clinical diagnosis. First order features always give accurate information about the medical image and useful for identification and analysis. In this work, first order features which entails the mean, median, mode, standard deviation, energy, Skewness, kurtosis, area, entropy were extracted from the infected malaria images. The corresponding results show a significant steady value which could be useful during classification of medical images. The work is ongoing, the aim of the work is to classify the malaria parasite and to come up with a diagnostic system that will be able to diagnose tissue and blood cell based diseases like malaria, Ebola etc.

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Index Terms

Computer Science
Information Sciences


Plasmodium Segmentation Energy Kurtosis Skewness Entropy