Blood image analysis to detect malaria using filtering image edges and classification

Memon, Murk Hassan, Saifullah Khanzada, Tariq Jamil, Memon, Sheeraz and Hassan, Syed Raheel (2019) Blood image analysis to detect malaria using filtering image edges and classification. TELKOMNIKA (Telecommunication Computing Electronics and Control), 17 (1). pp. 194-201. ISSN 1693-6930

Preview

PDF (11586-30249-1-PB) - Published Version Available under License Creative Commons Attribution. Download (967kB) | Preview

Abstract

Malaria is a most dangerous mosquito borne disease and its infection spread through the infected mosquito. It especially affects the pregnant females and Children less than 5 years age. Malarial species commonly occur in five different shapes, Therefore, to avoid this crucial disease the contemporary researchers have proposed image analysis based solutions to mitigate this death causing disease. In this work, we propose diagnosis algorithm for malaria which is implemented for testing and evaluation in Matlab. We use Filtering and classification along with median filter and SVM classifier. Our proposed method identifies the infected cells from rest of blood images. The Median filtering smoothing technique is used to remove the noise. The feature vectors have been proposed to find out the abnormalities in blood cells. Feature vectors include (Form factor, measurement of roundness, shape, count total number of red cells and parasites). Primary aim of this research is to diagnose malaria by finding out infected cells. However, many techniques and algorithm have been implemented in this field using image processing but accuracy is not up to the point. Our proposed algorithm got more efficient results along with high accuracy as compared to NCC and Fuzzy classifier used by the researchers recently.

Item Type:	Article
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Cyber Security Privacy and Trust Laboratory
Depositing User:	LivePure Connector
Date Deposited:	30 May 2022 11:30
Last Modified:	07 Oct 2023 01:14
URI:	https://ueaeprints.uea.ac.uk/id/eprint/85242
DOI:	10.12928/telkomnika.v17i1.11586

Downloads

Actions (login required)

View Item