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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Autores principales:	Peter U. Eze, Clement O. Asogwa
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	deep learning resource optimisation model quantisation malaria digital health edge devices Technology T Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/cc769e675a914f41b3f79f2f735f0751
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spelling	oai:doaj.org-article:cc769e675a914f41b3f79f2f735f07512021-11-25T16:46:20ZDeep Machine Learning Model Trade-Offs for Malaria Elimination in Resource-Constrained Locations10.3390/bioengineering81101502306-5354https://doaj.org/article/cc769e675a914f41b3f79f2f735f07512021-10-01T00:00:00Zhttps://www.mdpi.com/2306-5354/8/11/150https://doaj.org/toc/2306-5354The 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.Peter U. EzeClement O. AsogwaMDPI AGarticledeep learningresource optimisationmodel quantisationmalariadigital healthedge devicesTechnologyTBiology (General)QH301-705.5ENBioengineering, Vol 8, Iss 150, p 150 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep learning resource optimisation model quantisation malaria digital health edge devices Technology T Biology (General) QH301-705.5
spellingShingle	deep learning resource optimisation model quantisation malaria digital health edge devices Technology T Biology (General) QH301-705.5 Peter U. Eze Clement O. Asogwa Deep Machine Learning Model Trade-Offs for Malaria Elimination in Resource-Constrained Locations
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.
format	article
author	Peter U. Eze Clement O. Asogwa
author_facet	Peter U. Eze Clement O. Asogwa
author_sort	Peter U. Eze
title	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_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_sort	deep machine learning model trade-offs for malaria elimination in resource-constrained locations
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/cc769e675a914f41b3f79f2f735f0751
work_keys_str_mv	AT peterueze deepmachinelearningmodeltradeoffsformalariaeliminationinresourceconstrainedlocations AT clementoasogwa deepmachinelearningmodeltradeoffsformalariaeliminationinresourceconstrainedlocations
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Deep Machine Learning Model Trade-Offs for Malaria Elimination in Resource-Constrained Locations

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