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Research Journal of Pharmacy and Technology
Year : 2020, Volume : 13, Issue : 1
First page : ( 22) Last page : ( 26)
Print ISSN : 0974-3618. Online ISSN : 0974-360X.
Article DOI : 10.5958/0974-360X.2020.00004.9

Hookworm Detection from Wireless Capsule Endoscopy Images

Vadhana S Priya1,*, Anandhi P. Manjusha1,**, Sneha J. Adeline2,***

1Student, Sathyabama Institute of Science and Technology, Chennai

2Assistant Professor, Sathyabama Institute of Science and Technology, Chennai

*Corresponding Author E-mail: priyna1998@gmail.com

**manjusha.anandhi@gmail.com

***j.adelinesneha@gmail.com

Online published on 24 February, 2020.

Abstract

As one of the most common human Helminths, hookworm is a leading cause of child maternal and child morbidity. Hookworm infection seriously threatens human health, which will impair the physical and intellectual development of children. It is reported that hookworm has affected more than 600 million people worldwide. Wireless capsule Endoscopy (WCE) has become widely used diagnostic technique to examine inflammatory bowel diseases and disorders. Automatic hookworm detection is a challenging task due to poor quality of images, presence of extraneous matters, complex structure of gastrointestinal and diverse appearances in terms of Colour and texture. Hookworm infection in pregnancy can cause retarded growth of the fetus, premature birth and low birth weight. Hookworm in children can cause intellectual, cognitive and growth problem. This project is demonstrated with the detection of hookworm in human. In this project by observing its unique properties, we propose serials of novel techniques to capture its characteristics, aiming to reduce the number of images a clinician needs to review.

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Keywords

Hookworm detection, image processing, convolution neural network.

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