Clustering and Classification of a Qualitative Colorimetric Test

Marzia Hoque Tania, K T Lwin, Antesar M Shabut, Alamgir Hossain

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearch

Abstract

In this paper, we present machine learning based detection methods for a qualitative colorimetric test. Such an automatic system on mobile platform can emancipate the test result from the color perception of individuals and its subjectivity of interpretation, which can help millions of populations to access colorimetric test results for healthcare, allergen detection, forensic analysis, environmental monitoring and agricultural decision on point-of-care platforms. The case of plasmonic enzyme-linked immunosorbent assay (ELISA) based tuberculosis disease is utilized as a model experiment. Both supervised and unsupervised machine learning techniques are employed for the binary classification based on color moments. Using 10-fold cross validation, the ensemble bagged tree and k-nearest neighbors algorithm achieved 96.1% and 97.6% accuracy, respectively. The use of multi-layer perceptron with Bayesian regularization backpropagation provided 99.2% accuracy. Such high accuracy system can be trained off-line and deployed to mobile devices to produce an automatic colourimetric diagnostic decision anytime anywhere. © 2018 IEEE.
Original languageEnglish
Title of host publicationProceedings - 2018 International Conference on Computing, Electronics and Communications Engineering, iCCECE 2018
EditorsPeter S. Excell, Maaruf Ali, Andrew Jones, Safeeullah Soomro, Mahdi H. Miraz
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)978-1-5386-4904-6
DOIs
Publication statusPublished - 7 Mar 2019
Event2018 International Conference on Computing, Electronics & Communications Engineering - Southend, United Kingdom
Duration: 16 Aug 201817 Aug 2018

Publication series

NameProceedings - 2018 International Conference on Computing, Electronics and Communications Engineering, iCCECE 2018

Conference

Conference2018 International Conference on Computing, Electronics & Communications Engineering
Abbreviated titleiCCECE
CountryUnited Kingdom
City Southend
Period16/08/1817/08/18

Fingerprint

Learning systems
Allergens
Color
Multilayer neural networks
Backpropagation
Mobile devices
Assays
Enzymes
Monitoring
Experiments
Environmental analysis
Immunosorbents

Cite this

Hoque Tania, M., Lwin, K. T., Shabut, A. M., & Hossain, A. (2019). Clustering and Classification of a Qualitative Colorimetric Test. In P. S. Excell, M. Ali, A. Jones, S. Soomro, & M. H. Miraz (Eds.), Proceedings - 2018 International Conference on Computing, Electronics and Communications Engineering, iCCECE 2018 [8658480] (Proceedings - 2018 International Conference on Computing, Electronics and Communications Engineering, iCCECE 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/iCCECOME.2018.8658480
Hoque Tania, Marzia ; Lwin, K T ; Shabut, Antesar M ; Hossain, Alamgir. / Clustering and Classification of a Qualitative Colorimetric Test. Proceedings - 2018 International Conference on Computing, Electronics and Communications Engineering, iCCECE 2018. editor / Peter S. Excell ; Maaruf Ali ; Andrew Jones ; Safeeullah Soomro ; Mahdi H. Miraz. Institute of Electrical and Electronics Engineers Inc., 2019. (Proceedings - 2018 International Conference on Computing, Electronics and Communications Engineering, iCCECE 2018).
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title = "Clustering and Classification of a Qualitative Colorimetric Test",
abstract = "In this paper, we present machine learning based detection methods for a qualitative colorimetric test. Such an automatic system on mobile platform can emancipate the test result from the color perception of individuals and its subjectivity of interpretation, which can help millions of populations to access colorimetric test results for healthcare, allergen detection, forensic analysis, environmental monitoring and agricultural decision on point-of-care platforms. The case of plasmonic enzyme-linked immunosorbent assay (ELISA) based tuberculosis disease is utilized as a model experiment. Both supervised and unsupervised machine learning techniques are employed for the binary classification based on color moments. Using 10-fold cross validation, the ensemble bagged tree and k-nearest neighbors algorithm achieved 96.1{\%} and 97.6{\%} accuracy, respectively. The use of multi-layer perceptron with Bayesian regularization backpropagation provided 99.2{\%} accuracy. Such high accuracy system can be trained off-line and deployed to mobile devices to produce an automatic colourimetric diagnostic decision anytime anywhere. {\circledC} 2018 IEEE.",
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Hoque Tania, M, Lwin, KT, Shabut, AM & Hossain, A 2019, Clustering and Classification of a Qualitative Colorimetric Test. in PS Excell, M Ali, A Jones, S Soomro & MH Miraz (eds), Proceedings - 2018 International Conference on Computing, Electronics and Communications Engineering, iCCECE 2018., 8658480, Proceedings - 2018 International Conference on Computing, Electronics and Communications Engineering, iCCECE 2018, Institute of Electrical and Electronics Engineers Inc., 2018 International Conference on Computing, Electronics & Communications Engineering, Southend, United Kingdom, 16/08/18. https://doi.org/10.1109/iCCECOME.2018.8658480

Clustering and Classification of a Qualitative Colorimetric Test. / Hoque Tania, Marzia ; Lwin, K T; Shabut, Antesar M ; Hossain, Alamgir.

Proceedings - 2018 International Conference on Computing, Electronics and Communications Engineering, iCCECE 2018. ed. / Peter S. Excell; Maaruf Ali; Andrew Jones; Safeeullah Soomro; Mahdi H. Miraz. Institute of Electrical and Electronics Engineers Inc., 2019. 8658480 (Proceedings - 2018 International Conference on Computing, Electronics and Communications Engineering, iCCECE 2018).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearch

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AB - In this paper, we present machine learning based detection methods for a qualitative colorimetric test. Such an automatic system on mobile platform can emancipate the test result from the color perception of individuals and its subjectivity of interpretation, which can help millions of populations to access colorimetric test results for healthcare, allergen detection, forensic analysis, environmental monitoring and agricultural decision on point-of-care platforms. The case of plasmonic enzyme-linked immunosorbent assay (ELISA) based tuberculosis disease is utilized as a model experiment. Both supervised and unsupervised machine learning techniques are employed for the binary classification based on color moments. Using 10-fold cross validation, the ensemble bagged tree and k-nearest neighbors algorithm achieved 96.1% and 97.6% accuracy, respectively. The use of multi-layer perceptron with Bayesian regularization backpropagation provided 99.2% accuracy. Such high accuracy system can be trained off-line and deployed to mobile devices to produce an automatic colourimetric diagnostic decision anytime anywhere. © 2018 IEEE.

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M3 - Conference contribution

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PB - Institute of Electrical and Electronics Engineers Inc.

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Hoque Tania M, Lwin KT, Shabut AM, Hossain A. Clustering and Classification of a Qualitative Colorimetric Test. In Excell PS, Ali M, Jones A, Soomro S, Miraz MH, editors, Proceedings - 2018 International Conference on Computing, Electronics and Communications Engineering, iCCECE 2018. Institute of Electrical and Electronics Engineers Inc. 2019. 8658480. (Proceedings - 2018 International Conference on Computing, Electronics and Communications Engineering, iCCECE 2018). https://doi.org/10.1109/iCCECOME.2018.8658480