Cover Image

Autonomous self-exam monitoring for early diabetes detection

Diabetes can be prevented by early detection. In Malaysia, new case of diabetes is increasing year by year. Insufficient number of physicians tasked to treat a large number of patients will increase their burdens and also make them more stressed. An autonomous self-exam monitoring is developed in or...

Full description

Saved in:
Bibliographic Details
Main Authors: Rohana, Abdul Karim, Nur Alia Fatiha, Azhar, Nurul Wahidah, Arshad, Nor Farizan, Zakaria, M. Zabri, Abu Bakar
Format: Conference or Workshop Item
Language:English
Published: Springer Nature 2020
Subjects:
RA Public aspects of medicine
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/30437/1/Autonomous%20self-exam%20monitoring%20for%20early%20diabetes%20detection.pdf
http://umpir.ump.edu.my/id/eprint/30437/
https://doi.org/10.1007/978-981-15-2317-5_52
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.ump.umpir.30437
record_format eprints
spelling my.ump.umpir.304372021-01-11T06:20:33Z http://umpir.ump.edu.my/id/eprint/30437/ Autonomous self-exam monitoring for early diabetes detection Rohana, Abdul Karim Nur Alia Fatiha, Azhar Nurul Wahidah, Arshad Nor Farizan, Zakaria M. Zabri, Abu Bakar RA Public aspects of medicine TK Electrical engineering. Electronics Nuclear engineering Diabetes can be prevented by early detection. In Malaysia, new case of diabetes is increasing year by year. Insufficient number of physicians tasked to treat a large number of patients will increase their burdens and also make them more stressed. An autonomous self-exam monitoring is developed in order to assist the physicians in identifying diabetes at the early stage. Iris image is used to recognise the early detection of diabetes. Based on iridology theory, the image is evaluated by detecting the presence of broken tissues and change in colour pattern. It can be integrated with computer vision for an accurate identification of abnormality in iris image. This paper focuses on developing an iris imaging system that extracts the presence of orange pigmentation which is the sign of diabetes. This project comprises of three stages which are pre-processing, processing and post processing stage. The designed tool convert an iris image into new picture using image processing algorithms and analyses some changes in colour pattern and lastly diagnose whether it is diabetic or non-diabetic iris. The experimented images in this project are the iris image that was taken from public database UBIRIS.v1. At the end of this project, we discovered whether this system can detect the presence of broken tissues and change in colour pattern of iris or not. The final result shows the accuracy of 80% for detecting the orange pigmentation as the sign for early diabetes detection. Springer Nature 2020-03-24 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/30437/1/Autonomous%20self-exam%20monitoring%20for%20early%20diabetes%20detection.pdf Rohana, Abdul Karim and Nur Alia Fatiha, Azhar and Nurul Wahidah, Arshad and Nor Farizan, Zakaria and M. Zabri, Abu Bakar (2020) Autonomous self-exam monitoring for early diabetes detection. In: InECCE2019: Proceedings of the 5th International Conference on Electrical, Control & Computer Engineering, 29th July 2019 , Kuantan, Pahang, Malaysia. pp. 623-632., 632. ISBN 978-981-15-2317-5 https://doi.org/10.1007/978-981-15-2317-5_52
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic RA Public aspects of medicine
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle RA Public aspects of medicine
TK Electrical engineering. Electronics Nuclear engineering
Rohana, Abdul Karim
Nur Alia Fatiha, Azhar
Nurul Wahidah, Arshad
Nor Farizan, Zakaria
M. Zabri, Abu Bakar
Autonomous self-exam monitoring for early diabetes detection
description Diabetes can be prevented by early detection. In Malaysia, new case of diabetes is increasing year by year. Insufficient number of physicians tasked to treat a large number of patients will increase their burdens and also make them more stressed. An autonomous self-exam monitoring is developed in order to assist the physicians in identifying diabetes at the early stage. Iris image is used to recognise the early detection of diabetes. Based on iridology theory, the image is evaluated by detecting the presence of broken tissues and change in colour pattern. It can be integrated with computer vision for an accurate identification of abnormality in iris image. This paper focuses on developing an iris imaging system that extracts the presence of orange pigmentation which is the sign of diabetes. This project comprises of three stages which are pre-processing, processing and post processing stage. The designed tool convert an iris image into new picture using image processing algorithms and analyses some changes in colour pattern and lastly diagnose whether it is diabetic or non-diabetic iris. The experimented images in this project are the iris image that was taken from public database UBIRIS.v1. At the end of this project, we discovered whether this system can detect the presence of broken tissues and change in colour pattern of iris or not. The final result shows the accuracy of 80% for detecting the orange pigmentation as the sign for early diabetes detection.
format Conference or Workshop Item
author Rohana, Abdul Karim
Nur Alia Fatiha, Azhar
Nurul Wahidah, Arshad
Nor Farizan, Zakaria
M. Zabri, Abu Bakar
author_facet Rohana, Abdul Karim
Nur Alia Fatiha, Azhar
Nurul Wahidah, Arshad
Nor Farizan, Zakaria
M. Zabri, Abu Bakar
author_sort Rohana, Abdul Karim
title Autonomous self-exam monitoring for early diabetes detection
title_short Autonomous self-exam monitoring for early diabetes detection
title_full Autonomous self-exam monitoring for early diabetes detection
title_fullStr Autonomous self-exam monitoring for early diabetes detection
title_full_unstemmed Autonomous self-exam monitoring for early diabetes detection
title_sort autonomous self-exam monitoring for early diabetes detection
publisher Springer Nature
publishDate 2020
url http://umpir.ump.edu.my/id/eprint/30437/1/Autonomous%20self-exam%20monitoring%20for%20early%20diabetes%20detection.pdf
http://umpir.ump.edu.my/id/eprint/30437/
https://doi.org/10.1007/978-981-15-2317-5_52
_version_ 1822921859567452160
score 13.232414

Similar Items