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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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Bioengineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2306-5354/8/11/150 |
| _version_ | 1797511241366765568 |
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| author | Peter U. Eze Clement O. Asogwa |
| author_facet | Peter U. Eze Clement O. Asogwa |
| author_sort | Peter U. Eze |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:42:34Z |
| format | Article |
| id | doaj.art-cc769e675a914f41b3f79f2f735f0751 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-10T05:42:34Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-cc769e675a914f41b3f79f2f735f07512023-11-22T22:26:18ZengMDPI AGBioengineering2306-53542021-10-0181115010.3390/bioengineering8110150Deep Machine Learning Model Trade-Offs for Malaria Elimination in Resource-Constrained LocationsPeter U. Eze0Clement O. Asogwa1School of Computing and Information Systems, Faculty of Engineering and IT, University of Melbourne, Melbourne, VIC 3010, AustraliaSmart Electronics Systems Research Group, College of Engineering and Science, Victoria University, Melbourne, VIC 3011, AustraliaThe 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.https://www.mdpi.com/2306-5354/8/11/150deep learningresource optimisationmodel quantisationmalariadigital healthedge devices |
| spellingShingle | Peter U. Eze Clement O. Asogwa Deep Machine Learning Model Trade-Offs for Malaria Elimination in Resource-Constrained Locations Bioengineering deep learning resource optimisation model quantisation malaria digital health edge devices |
| 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 | deep learning resource optimisation model quantisation malaria digital health edge devices |
| url | https://www.mdpi.com/2306-5354/8/11/150 |
| work_keys_str_mv | AT peterueze deepmachinelearningmodeltradeoffsformalariaeliminationinresourceconstrainedlocations AT clementoasogwa deepmachinelearningmodeltradeoffsformalariaeliminationinresourceconstrainedlocations |