Assay type detection using advanced machine learning algorithms

Marzia Hoque Tania, Khin T. Lwin, Antesar M. Shabut, Kamal J. Abu-Hassan, M. Shamim Kaiser, M. A. Hossain

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

1 Citation (Scopus)

Abstract

The colourimetric analysis has been used in diversified fields for years. This paper provides a unique overview of colourimetric tests from the perspective of computer vision by describing different aspects of a colourimetric test in the context of image processing, followed by an investigation into the development of a colorimetric assay type detection system using advanced machine learning algorithms. To the best of our knowledge, this is the first attempt to define colourimetric assay types from the eyes of a machine and perform any colorimetric test using deep learning. This investigation utilizes the state-of-the-art pre-trained models of Convolutional Neural Network (CNN) to perform the assay type detection of an enzyme-linked immunosorbent assay (ELISA) and lateral flow assay (LFA). The ELISA dataset contains images of both positive and negative samples, prepared for the plasmonic ELISA based TB-antigen specific antibody detection. The LFA dataset contains images of the universal pH indicator paper of eight pH levels. It is noted that the pre-trained models offered 100% accurate visual recognition for the assay type detection. Such detection can assist novice users to initiate a colorimetric test using his/her personal digital devices. The assay type detection can also aid in calibrating an image-based colorimetric classification.

Original languageEnglish
Title of host publication2019 13th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2019
PublisherIEEE
ISBN (Electronic)9781728127415
DOIs
Publication statusPublished - Aug 2019
Event13th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2019 - Island of Ulkulhas, Maldives
Duration: 26 Aug 201928 Aug 2019

Publication series

Name2019 13th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2019

Conference

Conference13th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2019
CountryMaldives
CityIsland of Ulkulhas
Period26/08/1928/08/19

Keywords

  • Colorimetric test
  • Computer vision
  • Deep learning
  • Diagnosis
  • Point-of-care system
  • Transfer learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Software
  • Information Systems and Management

Cite this

Tania, M. H., Lwin, K. T., Shabut, A. M., Abu-Hassan, K. J., Kaiser, M. S., & Hossain, M. A. (2019). Assay type detection using advanced machine learning algorithms. In 2019 13th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2019 [8982449] (2019 13th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2019). IEEE. https://doi.org/10.1109/SKIMA47702.2019.8982449