Hand Portable Apparatus to Test Blood Cells Under Gold Standard of Microscope

Authors

  • Dhruva Iyer Arya Vidya Mandir (CICSE Board), JHUU

Keywords:

Malaria, Diagnosis, Rural Areas, Handheld Device, Deep Learning, Image Processing, Fourth order moment.

Abstract

Our biggest war was not against COVID-19 but against insufficient testing kits — one of the vital
components to confirm the infection and its spread. While we do not know when the COVID pandemic will end,
and when another one will hit. Timely testing and prevention are the only two key strategies that will save the
day. Taking lessons from COVID-19 which caused widespread loss of lives, we have developed a simple
apparatus that will enable testing of blood samples without dependency on huge diagnostic centers or
experienced senior doctors duringsuch pandemics. We took Malaria as a case study for two reasons – there is a
huge amount of data with respect to people suffering from this disease and blood cell image datasets for
processing and testing our hypothesis on Malaria. Many people die from malaria in rural areas even though they
have many RPD (Rapid Diagnostic Testing), but the gold standard of testing is a microscope which is not easily
available, and therefore we continued to find a more dependable and efficient way of detecting Malaria. The
setup consists of a smart mobile microscope which is a simple equipment that consists of ball lens, polarized
sheets, LED light and a blood sample that works on the principle of standard microscope that will give you
enlarged image of blood cells of parasites and with Deep Learning acting on this data we will get instant results
of classification into parasitized and non-parasitized cells. The mobile phone is trained to read and process blood
samples with the help of Deep Learning and image processing algorithm to give you a quick result of whether
the patient is having malaria or not without any prior knowledge in understanding blood cells. We use the fourth
order moment as an image processing technique to get the segmented images of malarial blood cells.

References

:

World Health Organization. World malaria

reports 2020. 20 years global progress &

Challenges. 30 November 2020.Gobal Report.

World Health Organization. Malaria

microscopy quality assurance manual-version 2.

World Health Organization;2016.

Dowling M.Shute G.A Comparative study

of thick and thin blood films in the diagnosis of

scanty malaria parasitemia. Bull World Health

Organ 1966;34:249.

iJournals: International Journal of Software & Hardware Research in Engineering (IJSHRE)

ISSN-2347-4890

Volume 9 Issue 5 May 2021

© 2021, iJournals All Rights Reserved www.ijournals.in

© 2020, iJournals All Rights Reserved www.ijournals.in

Page 131

Jan Z, Khan A, Sajjad M, Muhammad K,

Rho S, Mehmood I. A review on automated

diagnosis of malaria parasite in microscopic blood

smears images. Multimedia Tool Appl 2017;1-26.

Determining Cost Effectiveness of Malaria

Rapid Diagnostic Tests in Rural Areas with High

Prevalence.

WHO. World malaria report 2016. World

Health Organization;2016.

D. C. W. P. K. S. Tangpukdee N, "Malaria

diagnosis: a brief review. Korean J Parasitol.," 2009.

V. V. P. Janse CJ, " Flow cytometry in

malaria detection. Methods Cell Biol," p. 295–318.,

Malaria Diagnosis Using a Mobile Phone

Polarized Microscope.

Malaria Cell Images Dataset taken from

Kaggle.

A portable image-based cytometer for rapid

malaria detection and quantification.

Fiber array based hyperspectral Raman

imaging for chemical selective analysis of malariainfected red blood cells.

Extracting Focused Object from Low

Depth-of-Field Image Sequences.

Understanding the ResNet50 architecture.

ResNet in PyTorch. By PyTorch team.

H. M. S. C. A. F. Y. O. &. O. A. Zhu,

"Optofluidic fluorescent imaging cytometry on a cell

phone. Anal Chem 83,," p. 6641–6647, 2011.

A. Fitzpatrick, " 75% of World Has Access

to Mobile Phones (Study). Web 15 Jan. 2015. <

http://mashable.com/2012/07/17/mobile-phonesworldwide/>,," 2012.

J. Pramis, " Number of mobile phones to

exceed world population by 2014. Web 5 Jan. 2015.

<," http://www.digitaltrends.com/mobile/mobilephone-world-population-2014/>, Digital Trends

(2013).

S. S. T. W. T. D. K. E. A. &. O. A. Seo,

"Lensfree holographic imaging for on-chip

cytometry and diagnostics. Lab Chip 9,," p. 777–

, 2009.

Z. J. e. a. Smith, "Cell-Phone-Based

Platform for Biomedical Device Development and

Education Applications. PloS one 6,," 2011.

D. e. a. Tseng, "Lensfree microscopy on a

cellphone. Lab Chip 10,," p. 1787–1792, 2010.

H. Y. O. S. T. W. T. D. &. O. A. Zhu,

"Wide-field fluorescent microscopy on a cell-phone.

Conference proceedings:," no. IEEE Engineering in

Medicine .

A. W. G. L. D. &. R. R. Arpa, " In

Computer Vision and Pattern Recognition

Workshops (CVPRW), 2012," vol.

1109/CVPRW.2012.6239195., no. IEEE

Computer Society Conference on, , pp. 23-28, 2012.

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Published

2021-06-01

How to Cite

Dhruva Iyer. (2021). Hand Portable Apparatus to Test Blood Cells Under Gold Standard of Microscope. iJournals:International Journal of Software & Hardware Research in Engineering ISSN:2347-4890, 9(5). Retrieved from https://ijournals.in/journal/index.php/ijshre/article/view/24