Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Pathological Test Type and Chemical Detection Using Deep Neural Networks: A Case Study Using ELISA and LFA Assays

Version 1 : Received: 31 October 2020 / Approved: 2 November 2020 / Online: 2 November 2020 (09:56:04 CET)

How to cite: Tania, M..H.; Kaiser, M.S.; Abu-Hassan, K.J.; Hossain, M.A. Pathological Test Type and Chemical Detection Using Deep Neural Networks: A Case Study Using ELISA and LFA Assays. Preprints 2020, 2020110009 (doi: 10.20944/preprints202011.0009.v1). Tania, M..H.; Kaiser, M.S.; Abu-Hassan, K.J.; Hossain, M.A. Pathological Test Type and Chemical Detection Using Deep Neural Networks: A Case Study Using ELISA and LFA Assays. Preprints 2020, 2020110009 (doi: 10.20944/preprints202011.0009.v1).

Abstract

Purpose The gradual increase in geriatric issues and global imbalance of the ratio between patients and healthcare professionals has 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 an 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, etc. 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 i) classifies the type of the assay, and ii) 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 our 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.

Subject Areas

Computer Vision; Machine Learning; Colourimetric Test; Pre-trained Model; Point-of-Care System; Diagnosis

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