Development of full-field optical spatial coherence tomography system for automated identification of malaria using the multilevel ensemble classifier.

Abstract:

:Malaria is a life-threatening infectious blood disease affecting humans and other animals caused by parasitic protozoans belonging to the Plasmodium type especially in developing countries. The gold standard method for the detection of malaria is through the microscopic method of chemically treated blood smears. We developed an automated optical spatial coherence tomographic system using a machine learning approach for a fast identification of malaria cells. In this study, 28 samples (15 healthy, 13 malaria infected stages of red blood cells) were imaged by the developed system and 13 features were extracted. We designed a multilevel ensemble-based classifier for the quantitative prediction of different stages of the malaria cells. The proposed classifier was used by repeating k-fold cross validation dataset and achieve a high-average accuracy of 97.9% for identifying malaria infected late trophozoite stage of cells. Overall, our proposed system and multilevel ensemble model has a substantial quantifiable potential to detect the different stages of malaria infection without staining or expert.

journal_name

J Biophotonics

journal_title

Journal of biophotonics

authors

Singla N,Srivastava V,Mehta DS

doi

10.1002/jbio.201700279

subject

Has Abstract

pub_date

2018-05-01 00:00:00

pages

e201700279

issue

5

eissn

1864-063X

issn

1864-0648

journal_volume

11

pub_type

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