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Sequential classification system for recognition of malaria infection using peripheral blood cell images.

Authors
  • Molina, Angel1
  • Alférez, Santiago2
  • Boldú, Laura3
  • Acevedo, Andrea3, 4
  • Rodellar, José4
  • Merino, Anna3, 5
  • 1 Biochemistry and Molecular Genetics, Biomedical Diagnostic Center, Hospital Clinic de Barcelona, Barcelona, Spain [email protected] , (Spain)
  • 2 School of Engineering, Science and Technology, Universidad del Rosario Facultad de Ciencias Naturales y Matemáticas, Bogota, Cundinamarca, Colombia. , (Colombia)
  • 3 Biochemistry and Molecular Genetics, Biomedical Diagnostic Center, Hospital Clinic de Barcelona, Barcelona, Spain. , (Spain)
  • 4 Matemáticas CoDAlab, Universitat Politecnica de Catalunya, Barcelona, Catalunya, Spain. , (Spain)
  • 5 Hospital Clinic de Barcelona, Barcelona, Catalunya, Spain. , (Spain)
Type
Published Article
Journal
Journal of Clinical Pathology
Publisher
BMJ
Publication Date
Oct 01, 2020
Volume
73
Issue
10
Pages
665–670
Identifiers
DOI: 10.1136/jclinpath-2019-206419
PMID: 32179558
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Morphological recognition of red blood cells infected with malaria parasites is an important task in the laboratory practice. Nowadays, there is a lack of specific automated systems able to differentiate malaria with respect to other red blood cell inclusions. This study aims to develop a machine learning approach able to discriminate parasitised erythrocytes not only from normal, but also from other erythrocyte inclusions, such as Howell-Jolly and Pappenheimer bodies, basophilic stippling as well as platelets overlying red blood cells. A total of 15 660 erythrocyte images from 87 smears were segmented using histogram thresholding and watershed techniques, which allowed the extraction of 2852 colour and texture features. Dataset was split into a training and assessment sets. Training set was used to develop the whole system, in which several classification approaches were compared with obtain the most accurate recognition. Afterwards, the recognition system was evaluated with the assessment set, performing two steps: (1) classifying each individual cell image to assess the system's recognition ability and (2) analysing whole smears to obtain a malaria infection diagnosis. The selection of the best classification approach resulted in a final sequential system with an accuracy of 97.7% for the six groups of red blood cell inclusions. The ability of the system to detect patients infected with malaria showed a sensitivity and specificity of 100% and 90%, respectively. The proposed method achieves a high diagnostic performance in the recognition of red blood cell infected with malaria, along with other frequent erythrocyte inclusions. © Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.

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