Performance of SVM and ANFIS for Classification of Malaria Parasite and Its Life-Cycle-Stages in Blood Smear
A method to classify Plasmodium malaria disease along with its life stage is presented. The geometry and texture features are used as Plasmodium features for classification. The geometry features are area and perimeters. The texture features are computed from GLCM matrices. The support vector machine (SVM) classifier is employed for classifying the Plasmodium and its life stage into 12 classes. Experiments were conducted using 600 images of blood samples. The SVM with RBF kernel yields an accuracy of 99.1%, while the ANFIS gives an accuracy of 88.5%.
KeywordsMalaria Geometry Texture GLCM RBF
The authors would like to thank the Directorate General of Higher Education, the Ministry of Research and Higher Education of the Republic of Indonesia for sponsoring this research. The authors would also like to thank the parasitology Health Laboratory of the North Sumatra Province and Bina Medical Support Services (BPPM), Jakarta, for supporting this research.
- 2.Jain, P., Chakma, B., Patra, S., Goswami, P.: Potential biomarkers and their applications for rapid and reliable detection of malaria. BioMed Res. Int., 201–221 (2014). https://doi.org/10.1155/2014/852645
- 4.Tek, F.B., Dempster, A.G., Kale, I.: Malaria parasite detection in peripheral blood images. In: 17th International Conference British Machine Vision Conference Proceedings, pp. 347–356. British Machine Vision Association, Edinburgh (2006). https://doi.org/10.1109/ACCESS.2017.2705642
- 7.Nugroho, H.A., Akbar, S.A., Muhandarwari, E.E.H.: Feature extraction and classification for detection malaria parasites in thin blood smear. In: 2nd International Conference on Information Technology, Computer, and Electrical Engineering Proceedings, pp. 198–201. IEEE, Semarang (2015). https://doi.org/10.1109/ICITACEE.2015.7437798
- 8.Khatri, E.K.M., Ratnaparkhe, V.R., Agrawal, S.S., Bhalchandra, A.S.: Image processing approach for malaria parasite identification. Int. J. Comput. Appl. 5–7 (2014)Google Scholar
- 9.Kumar, A., Choudhary, A., Tembhare, P.U., Pote, C.R.: Enhanced identification of malarial infected objects using Otsu algorithm from thin smear digital images. Int. J. Latest Res. Sci. Technol. 1(159), 2278–5299 (2012)Google Scholar
- 10.Ahirwar, N., Pattnaik, S., Acharya, B.: Advanced image analysis based system for automatic detection and classification of malaria parasite in blood images. Int. J. Inf. Technol. Knowl. Manag. 5(1), 59–64 (2012)Google Scholar
- 12.Bhavsar, T.H., Panchal, M.H.: A review on support vector machine for data classification. Int. J. Adv. Res. Comput. Eng. Technol. 1(10), 185–189 (2012)Google Scholar