Classification of Malaria Parasite Plasmodium Falciparum Based on Blood Smear Images Using Support Vector Machine Approach

Nur Chamidah, Toha Saifudin, Riries Rulaningtyas, Adam Anargya Mawardi, Puspa Wardhani, I. Nyoman Budiantara, Naufal Ramadhan Al Akhwal Siregar

Research output: Contribution to journalArticlepeer-review

Abstract

Malaria remains a significant global health problem, especially in tropical and subtropical regions. The disease results in a large number of clinical cases and deaths each year, with high-risk groups including infants, toddlers, and pregnant women. Accurate and rapid diagnosis is a key factor in treating this disease. To address this problem, this research aims to develop an automatic system for classifying the malaria parasite Plasmodium Falciparum based on blood smear images. The method used includes image feature selection using Principal Component Analysis (PCA) and the Support Vector Machine (SVM) approach for classification. The results showed that in the image feature selection process, the normal malaria category showed typical characteristics with PC1 and PC2 values that tended to be negative and scattered, while the parasitic malaria category showed greater variability in the PC1 and PC2 components. Furthermore, evaluation of the accuracy of the classification system using SVM with three different kernel types shows promising results. The average accuracy through K-fold cross-validation for the polyinomial, linear, and radial basis function kernels is 96,7 %, 98,9 %, and 94,4 %, respectively. These results highlight the significant potential of utilizing SVM in the classification of the malaria parasite Plasmodium Falciparum based on blood smear images.

Original languageEnglish
Article number568
JournalData and Metadata
Volume4
DOIs
Publication statusPublished - 1 Jan 2025

Keywords

  • Malaria
  • Parasite Classification
  • Principal Component Analysis
  • Support Vector Machine

Fingerprint

Dive into the research topics of 'Classification of Malaria Parasite Plasmodium Falciparum Based on Blood Smear Images Using Support Vector Machine Approach'. Together they form a unique fingerprint.

Cite this