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
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Abstract
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.
| Item Type: | Conference or Workshop Item (Lecture) |
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| Additional Information: | Part of the Lecture Notes in Electrical Engineering book series |
| Uncontrolled Keywords: | Iris diabetes; Image processing; Classification |
| Subjects: | R Medicine > RA Public aspects of medicine T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Faculty of Electrical & Electronic Engineering Institute of Postgraduate Studies |
| Depositing User: | Mrs Norsaini Abdul Samat |
| Date Deposited: | 11 Jan 2021 06:20 |
| Last Modified: | 11 Jan 2021 06:20 |
| URI: | http://umpir.ump.edu.my/id/eprint/30437 |
| Download Statistic: | View Download Statistics |
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