An Automated Colourimetric Test by Computational Chromaticity Analysis: A Case Study of Tuberculosis Test

Tania, Marzia Hoque; Lwin, Khin T.; Abuhassan, Kamal; Bakhori, Noremylia Mohd; Azmi, Umi Zulaikha Mohd; Yusof, Nor Azah

doi:10.1007/978-3-319-60816-7_38

Cited by 4 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The pathological test of the gold nanoparticles (AuNP)-based plasmonic ELISA for TB-antigen-specific antibody detection, referred to as the TB test, is one of the case studies for assay type detection in this paper. The description of the sample preparation for the TB test is provided in (Abuhassan et al , 2017; Tania et al , 2017; Bakhori et al , 2018). However, the development of biosensor is not the focus of this work.…”

Section: Methodsmentioning

confidence: 99%

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

Tania

Kaiser

Abuhassan

2020

JEIM

View full text Add to dashboard Cite

PurposeThe 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/approachThe 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.FindingsThis 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/valueTo 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.

show abstract

Section: Methodsmentioning

confidence: 99%

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

Tania

Kaiser

Abuhassan

2020

JEIM

View full text Add to dashboard Cite

show abstract

“…One is the Gold Nanoparticles (AuNP) based plasmonic ELISA for TB-antigen specific antibody detection, referred to as the TB-test in this paper. The description of the sample preparation for the TB-test is provided in [9], [41], [42]. However, development of the biosensor is not the focus of this work.…”

Section: A Data Collectionmentioning

confidence: 99%

Assay Type Detection Using Advanced Machine Learning Algorithms

Tania

Lwin

Shabut

et al. 2019

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

An intelligent mobile-enabled expert system for tuberculosis disease diagnosis in real time

Shabut

Tania

Lwin

et al. 2018

Expert Systems with Applications

View full text Add to dashboard Cite

An Automated Colourimetric Test by Computational Chromaticity Analysis: A Case Study of Tuberculosis Test

Cited by 4 publications

References 16 publications

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

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

Assay Type Detection Using Advanced Machine Learning Algorithms

An intelligent mobile-enabled expert system for tuberculosis disease diagnosis in real time

Contact Info

Product

Resources

About