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A Deep Learning Approach for Segmentation of Red Blood Cell Images and Malaria Detection
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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517192/ https://www.ncbi.nlm.nih.gov/pubmed/33286429 http://dx.doi.org/10.3390/e22060657 |
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author | Delgado-Ortet, Maria Molina, Angel Alférez, Santiago Rodellar, José Merino, Anna |
author_facet | Delgado-Ortet, Maria Molina, Angel Alférez, Santiago Rodellar, José Merino, Anna |
author_sort | Delgado-Ortet, Maria |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7517192 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75171922020-11-09 A Deep Learning Approach for Segmentation of Red Blood Cell Images and Malaria Detection Delgado-Ortet, Maria Molina, Angel Alférez, Santiago Rodellar, José Merino, Anna Entropy (Basel) Article 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. MDPI 2020-06-13 /pmc/articles/PMC7517192/ /pubmed/33286429 http://dx.doi.org/10.3390/e22060657 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Delgado-Ortet, Maria Molina, Angel Alférez, Santiago Rodellar, José Merino, Anna A Deep Learning Approach for Segmentation of Red Blood Cell Images and Malaria Detection |
title | A Deep Learning Approach for Segmentation of Red Blood Cell Images and Malaria Detection |
title_full | A Deep Learning Approach for Segmentation of Red Blood Cell Images and Malaria Detection |
title_fullStr | A Deep Learning Approach for Segmentation of Red Blood Cell Images and Malaria Detection |
title_full_unstemmed | A Deep Learning Approach for Segmentation of Red Blood Cell Images and Malaria Detection |
title_short | A Deep Learning Approach for Segmentation of Red Blood Cell Images and Malaria Detection |
title_sort | deep learning approach for segmentation of red blood cell images and malaria detection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7517192/ https://www.ncbi.nlm.nih.gov/pubmed/33286429 http://dx.doi.org/10.3390/e22060657 |
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