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Deep Machine Learning Model Trade-Offs for Malaria Elimination in Resource-Constrained Locations

The success of deep machine learning (DML) models in gaming and robotics has increased its trial in clinical and public healthcare solutions. In applying DML to healthcare problems, a special challenge of inadequate electrical energy and computing resources exists in regional and developing areas of...

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Detalles Bibliográficos
Autores principales: Eze, Peter U., Asogwa, Clement O.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8614791/
https://www.ncbi.nlm.nih.gov/pubmed/34821716
http://dx.doi.org/10.3390/bioengineering8110150
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author Eze, Peter U.
Asogwa, Clement O.
author_facet Eze, Peter U.
Asogwa, Clement O.
author_sort Eze, Peter U.
collection PubMed
description The success of deep machine learning (DML) models in gaming and robotics has increased its trial in clinical and public healthcare solutions. In applying DML to healthcare problems, a special challenge of inadequate electrical energy and computing resources exists in regional and developing areas of the world. In this paper, we evaluate and report the computational and predictive performance design trade-offs for four candidate deep learning models that can be deployed for rapid malaria case finding. The goal is to maximise malaria detection accuracy while reducing computing resource and energy consumption. Based on our experimental results using a blood smear malaria test data set, the quantised versions of Basic Convolutional Neural Network (B-CNN) and MobileNetV2 have better malaria detection performance (up to 99% recall), lower memory usage (2MB 8-bit quantised model) and shorter inference time (33–95 microseconds on mobile phones) than VGG-19 fine-tuned and quantised models. Hence, we have implemented MobileNetV2 in our mobile application as it has even a lower memory requirement than B-CNN. This work will help to counter the negative effects of COVID-19 on the previous successes towards global malaria elimination.
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spelling pubmed-86147912021-11-26 Deep Machine Learning Model Trade-Offs for Malaria Elimination in Resource-Constrained Locations Eze, Peter U. Asogwa, Clement O. Bioengineering (Basel) Article The success of deep machine learning (DML) models in gaming and robotics has increased its trial in clinical and public healthcare solutions. In applying DML to healthcare problems, a special challenge of inadequate electrical energy and computing resources exists in regional and developing areas of the world. In this paper, we evaluate and report the computational and predictive performance design trade-offs for four candidate deep learning models that can be deployed for rapid malaria case finding. The goal is to maximise malaria detection accuracy while reducing computing resource and energy consumption. Based on our experimental results using a blood smear malaria test data set, the quantised versions of Basic Convolutional Neural Network (B-CNN) and MobileNetV2 have better malaria detection performance (up to 99% recall), lower memory usage (2MB 8-bit quantised model) and shorter inference time (33–95 microseconds on mobile phones) than VGG-19 fine-tuned and quantised models. Hence, we have implemented MobileNetV2 in our mobile application as it has even a lower memory requirement than B-CNN. This work will help to counter the negative effects of COVID-19 on the previous successes towards global malaria elimination. MDPI 2021-10-21 /pmc/articles/PMC8614791/ /pubmed/34821716 http://dx.doi.org/10.3390/bioengineering8110150 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Eze, Peter U.
Asogwa, Clement O.
Deep Machine Learning Model Trade-Offs for Malaria Elimination in Resource-Constrained Locations
title Deep Machine Learning Model Trade-Offs for Malaria Elimination in Resource-Constrained Locations
title_full Deep Machine Learning Model Trade-Offs for Malaria Elimination in Resource-Constrained Locations
title_fullStr Deep Machine Learning Model Trade-Offs for Malaria Elimination in Resource-Constrained Locations
title_full_unstemmed Deep Machine Learning Model Trade-Offs for Malaria Elimination in Resource-Constrained Locations
title_short Deep Machine Learning Model Trade-Offs for Malaria Elimination in Resource-Constrained Locations
title_sort deep machine learning model trade-offs for malaria elimination in resource-constrained locations
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8614791/
https://www.ncbi.nlm.nih.gov/pubmed/34821716
http://dx.doi.org/10.3390/bioengineering8110150
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