Clustering and Classification of a Qualitative Colorimetric Test

Hoque Tania, Marzia and Lwin, Khin T. and Shabut, Antesar M. and Hossain, Mohammed Alamgir (2019) Clustering and Classification of a Qualitative Colorimetric Test. In: 2018 International Conference on Computing, Electronics & Communications Engineering (iCCECE), Southend, UK.

[img]
Preview
Text
Accepted Version
Available under the following license: Creative Commons Attribution Non-commercial No Derivatives.

Download (421kB) | Preview
Official URL: http://dx.doi.org/10.1109/iccecome.2018.8658480

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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: © 2019 IEEE.Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: machine learning, artificial neural network, multi-layer perceptron, Bayesian regularization, diagnostic decision, colorimetric detection, tuberculosis
Faculty: Faculty of Science & Engineering
SWORD Depositor: Symplectic User
Depositing User: Symplectic User
Date Deposited: 13 Mar 2019 11:22
Last Modified: 14 Nov 2019 16:08
URI: http://arro.anglia.ac.uk/id/eprint/704171

Actions (login required)

Edit Item Edit Item