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Pathological test type and chemical detection using deep neural networks: a case study using ELISA and LFA assays

Marzia Hoque Tania (Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, UK)
M. Shamim Kaiser (Institute of Information Technology, Jahangirnagar University, Savar, Bangladesh)
Kamal Abu-Hassan (Department of Physics, University of Bath, Bath, UK)
M. A. Hossain (Department of Computer Science and Information Systems, Teesside University, Middlesbrough, UK)

Journal of Enterprise Information Management

ISSN: 1741-0398

Article publication date: 3 August 2020

Issue publication date: 24 April 2023

317

Abstract

Purpose

The gradual increase in geriatric issues and global imbalance of the ratio between patients and healthcare professionals have created a demand for intelligent systems with the least error-prone diagnosis results to be used by less medically trained persons and save clinical time. This paper aims at investigating the development of image-based colourimetric analysis. The purpose of recognising such tests is to support wider users to begin a colourimetric test to be used at homecare settings, telepathology and so on.

Design/methodology/approach

The concept of an automatic colourimetric assay detection is delivered by utilising two cases. Training deep learning (DL) models on thousands of images of these tests using transfer learning, this paper (1) classifies the type of the assay and (2) classifies the colourimetric results.

Findings

This paper demonstrated that the assay type can be recognised using DL techniques with 100% accuracy within a fraction of a second. Some of the advantages of the pre-trained model over the calibration-based approach are robustness, readiness and suitability to deploy for similar applications within a shorter period of time.

Originality/value

To the best of the authors’ knowledge, this is the first attempt to provide colourimetric assay type classification (CATC) using DL. Humans are capable to learn thousands of visual classifications in their life. Object recognition may be a trivial task for humans, due to photometric and geometric variabilities along with the high degree of intra-class variabilities, it can be a challenging task for machines. However, transforming visual knowledge into machines, as proposed, can support non-experts to better manage their health and reduce some of the burdens on experts.

Keywords

Acknowledgements

The data set of this paper was collected as part of the doctoral research of the first author, funded by Erasmus Mundus Partnerships Action 2 “FUSION” (Featured Europe and South Asia mObility Network). Grant reference number: 2013–3254 1/001001. The authors extend their gratitude to Dr Khin Lwin and Dr Antesar Shabut for their support in the PhD project. The authors thank Prof Nor Azah Yusof and her team, Universiti Putra Malaysia for their support to collect the ELISA dataset. The original ELISA data set was generated as part of the project named “TB-Test - A smart mobile enabled scheme for tuberculosis testing”, supported by British Council Newton Institutional Links and Newton-Ungku Omar Fund (Grant ID: 216385726). The authors also thank Dr Mohammad Najlah and Mr Paul Cotton, Anglia Ruskin University for their support to conduct the laboratory experiments on LFA.

Citation

Hoque Tania, M., Kaiser, M.S., Abu-Hassan, K. and Hossain, M.A. (2023), "Pathological test type and chemical detection using deep neural networks: a case study using ELISA and LFA assays", Journal of Enterprise Information Management, Vol. 36 No. 3, pp. 790-817. https://doi.org/10.1108/JEIM-01-2020-0038

Publisher

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Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

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