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Open AccessArticle

A Deep Learning Approach for Segmentation of Red Blood Cell Images and Malaria Detection

1
Core Laboratory, Biochemistry and Molecular Genetics, Biomedical Diagnostic Center, Hospital Clínic of Barcelona, 08036 Barcelona, Spain
2
Applied Mathematics and Computer Science, School of Engineering, Science and Technology, Universidad del Rosario, Bogotá 111711, Colombia
3
Department of Mathematics, Technical University of Catalonia, 08019 Barcelona, Spain
*
Authors to whom correspondence should be addressed.
Entropy 2020, 22(6), 657; https://doi.org/10.3390/e22060657
Received: 15 May 2020 / Revised: 5 June 2020 / Accepted: 11 June 2020 / Published: 13 June 2020
Malaria is an endemic life-threating disease caused by the unicellular protozoan parasites of the genus Plasmodium. Confirming the presence of parasites early in all malaria cases ensures species-specific antimalarial treatment, reducing the mortality rate, and points to other illnesses in negative cases. However, the gold standard remains the light microscopy of May-Grünwald–Giemsa (MGG)-stained thin and thick peripheral blood (PB) films. This is a time-consuming procedure, dependent on a pathologist’s skills, meaning that healthcare providers may encounter difficulty in diagnosing malaria in places where it is not endemic. This work presents a novel three-stage pipeline to (1) segment erythrocytes, (2) crop and mask them, and (3) classify them into malaria infected or not. The first and third steps involved the design, training, validation and testing of a Segmentation Neural Network and a Convolutional Neural Network from scratch using a Graphic Processing Unit. Segmentation achieved a global accuracy of 93.72% over the test set and the specificity for malaria detection in red blood cells (RBCs) was 87.04%. This work shows the potential that deep learning has in the digital pathology field and opens the way for future improvements, as well as for broadening the use of the created networks. View Full-Text
Keywords: deep learning; malaria detection; red blood cell (RBC) segmentation; blood cell classification; convolutional neural networks deep learning; malaria detection; red blood cell (RBC) segmentation; blood cell classification; convolutional neural networks
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Figure 1

  • Externally hosted supplementary file 1
    Doi: 10.5281/zenodo.3828050
    Link: https://zenodo.org/record/3828050#.Xr8BcC-w1sM
    Description: Source Code of a Digital Pathology System for RBC Segmentation and Malaria Detection through Deep Learning
  • Externally hosted supplementary file 2
    Doi: 10.17632/c37wnbbd3c.1
    Link: https://data.mendeley.com/datasets/c37wnbbd3c/1
    Description: Dataset A: 186 digital images of MGG-stained blood smears from five patients with hereditary spherocytosis
  • Externally hosted supplementary file 3
    Doi: 10.17632/2v6h4j48cx.1
    Link: https://data.mendeley.com/datasets/2v6h4j48cx/1
    Description: Dataset B: 331 digital images of MGG-stained blood smears from five malaria-infected patients
MDPI and ACS Style

Delgado-Ortet, M.; Molina, A.; Alférez, S.; Rodellar, J.; Merino, A. A Deep Learning Approach for Segmentation of Red Blood Cell Images and Malaria Detection. Entropy 2020, 22, 657.

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