dc.contributor.author |
Mudugamuwa, A.P. |
|
dc.contributor.author |
Hettiarachchi, S.P. |
|
dc.contributor.author |
Basnayake, B. A. D. J. C. K. |
|
dc.date.accessioned |
2022-03-04T07:22:08Z |
|
dc.date.available |
2022-03-04T07:22:08Z |
|
dc.date.issued |
2021-12-04 |
|
dc.identifier.citation |
Mudugamuwa, A.P., et al.(2021).Review on Photomicrography based Full Blood Count (FBC) Testing and Recent Advancements, Advances in Technology,1(3), 422-453 |
en_US |
dc.identifier.uri |
http://dr.lib.sjp.ac.lk/handle/123456789/10413 |
|
dc.description.abstract |
With advancements in related sub-fields, research on photomicrography in life science is emerging and this is a review on its application towards human full blood count testing which is a primary test in medical practices. For a prolonged period of time, analysis of blood samples is the basis for bio medical observations of living creatures. Cell size, shape, constituents, count, ratios are few of the features identified using DIP based analysis and these features provide an overview of the state of human body which is important in identifying present medical conditions and indicating possible future complications. In addition, functionality of the immune system is observed using results of blood tests. In FBC tests, identification of different blood cell types and counting the number of cells of each type is required to obtain results. Literature discuss various techniques and methods and this article presents an insightful review on human blood cell morphology, photomicrography, digital image processing of photomicrographs, feature extraction and classification, and recent advances. Integration of emerging technologies such as microfluidics, micro-electromechanical systems, and artificial intelligence based image processing algorithms and classifiers with cell sensing have enabled exploration of novel research directions in blood testing applications. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
Faculty of Technology, USJ |
en_US |
dc.subject |
cell identification, cell classification, deep learning, photomicrograph analysis, lab on a chip, computer vision |
en_US |
dc.title |
Review on Photomicrography based Full Blood Count (FBC) Testing and Recent Advancements |
en_US |
dc.type |
Article |
en_US |
dc.identifier.doi |
https://doi.org/10.31357/ait.v1i2.5252 |
en_US |