Online ISSN: 2515-8260

Keywords : Malarial parasite


FOURIER–MELLIN TRANSFORM FEATURES FOR MALARIA PARASITES CLASSIFICATION USING MICROSCOPIC IMAGES

R. Charanya; J. Josphin Mary; G. Sridevi; V. Shanthi

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 9, Pages 1426-1430
DOI: 10.31838/ejmcm.07.09.150

Malaria is an infectious disease transmitted by mosquitoes that affects humans and other animals. Malaria is responsible for the effects of fever, tiredness, vomiting and headaches. Yellow skin, convulsions, a coma, or death can lead to severe cases. Symptoms usually start 10-15 days after a mosquito is bitten. The early diagnosis is required for malaria. In this study, the automatic classification of malaria system is discussed. Initially, the input images are given to Fourier–Mellin transform for feature extraction and Support Vector Machine (SVM) classifier is used for classification. The performance of malaria system produces the classification accuracy of 92%using SVM classifier.

MALARIA PARASITE CLASSIFICATION USING ENERGY BASED KNN CLASSIFIER

R. Charanya; J. Josphin Mary; G. Sridevi; V. Shanthi

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 9, Pages 1422-1425
DOI: 10.31838/ejmcm.07.09.149

Plasmodium is a type of unicellular eukaryote compulsory for vertebrates or insect parasites. The early diagnosis is required for malaria. In this study, the automatic classification of malaria system is discussed. Initially, the input images are given to gaussian filter, then energy feature is used for feature extraction and K-Nearest Neighbor (KNN) classifier is used for classification. The performance of malaria system produces the specificity of classification 93%using KNN classifier.

Automated Diagnosis of Malarial Parasite in Red Blood Cells

Mr K.P.K Devan; Dr G. S. Anandha Mala; Deepthi Salunkey. K; Grace Cynthia. R; Madhumitha. J

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 4, Pages 2718-2725

The traditional system for detecting the infection has been the manual process of diagnosing the stained slides under a microscope. This manual process might consume more time for producing the results and the availability of medical experts is not always assured. Considering this as the primary concern we proposed a strategy which limits the human error while recognizing the presence of malarial parasite in the blood sample by using Image Processing. Hence by automating the diagnosis process, results can be acquired relatively quicker and more accuracy can be expected. The technologies and techniques to patently extract the required features and efficiently classify the infected samples are surveyed. This paper presents a survey of various approaches to automate the detection and classification of infected and uninfected cells.