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

Abstract

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.

Whole surface defect detection method for bearing rings based on machine vision

“…The production process has good flexibility and automation and has good application prospects. It is widely used in many fields such as industry [8,9], agriculture [10][11][12], medical treatment [13][14][15], and transportation [16][17][18].…”

Assessing organizational health-analytics readiness: artifacts based on elaborated action design method

“…, 2014). HA overlays the volume, velocity and variety of data created by the other HIT systems, to provide clinical (Hoque Tania et al. , 2020), operational (Heshmat et al.…”

“…Organizations adopt HA, owing to its potential to create value in process improvement, patient care and clinical outcomes (Cortada et al, 2012;Raghupathi and Raghupathi, 2014;Ward et al, 2014). HA overlays the volume, velocity and variety of data created by the other HIT systems, to provide clinical (Hoque Tania et al, 2020), operational (Heshmat et al, 2018), administrative (Chalmers et al, 2018) and strategic (Zor and Çebi, 2018) analysis and insights needed to make crucial decisions (Raghupathi and Raghupathi, 2014). The analytical approaches include statistical, contextual, quantitative, predictive and cognitive models (Cortada et al, 2012;Ward et al, 2014).…”