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.
|Title of host publication||2019 13th International Conference on Software, Knowledge, Information Management and Applications|
|Publication status||Published - 6 Feb 2020|
|Event||13th International Conference on Software, Knowledge, Information Management and Applications and the International Workshop on Applied Artificial Intelligence - Island of UKULHAS, Maldives, Island of UKULHAS, Maldives|
Duration: 26 Aug 2019 → 28 Aug 2019
|Conference||13th International Conference on Software, Knowledge, Information Management and Applications and the International Workshop on Applied Artificial Intelligence|
|City||Island of UKULHAS|
|Period||26/08/19 → 28/08/19|