Assay Type Detection Using Advanced Machine Learning Algorithms

Hoque Tania, Marzia and Lwin, Khin T. and Shabut, Antesar M. and Abu-Hassan, Kamal J. and Kaiser, M. Shamim and Hossain, Mohammed Alamgir (2020) Assay Type Detection Using Advanced Machine Learning Algorithms. In: 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), Ulkulhas, Maldives.

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Official URL: https://doi.org/10.1109/SKIMA47702.2019.8982449

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.

Item Type: Conference or Workshop Item (Paper)
Keywords: computer vision, deep learning, transfer learning
Faculty: Faculty of Health, Education, Medicine & Social Care
Depositing User: Ian Walker
Date Deposited: 13 Mar 2020 15:54
Last Modified: 09 Sep 2021 18:53
URI: https://arro.anglia.ac.uk/id/eprint/705299

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