Designing Diabetes Mellitus Detection System Based on Iridology with Convolutional Neural Network Modeling

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Diabetes mellitus is one of the uncontagious diseases with the highest mortality rate in the world. It happens because of the increased risk of complications caused by the disease. One of the preventative ways is to do early detection, one of which is by using the iridology method. The method detects damage to the body's organs through the signs that appear on the iris. The paper has introduced a Diabetes Mellitus Detection System to classify diabetes using a Convolutional Neural Network (CNN). The proposed method removed the pupil segmentation step that is important in the traditional machine learning classification system. The squared pupil image size 720×360 pixel was trained using Adam's algorithm with a learning rate of 0.001 to develop the CNN model. The pupil image was collected using Iriscope Iris Analyzer Iridology 9822U camera. The dataset consists of 35 healthy and 14 diabetes subjects that repeat three times of each person. The proposed approach has an accuracy of 96.43% that better performance compared to traditional machine learning.

Original languageEnglish
Title of host publicationICICoS 2020 - Proceeding
Subtitle of host publication4th International Conference on Informatics and Computational Sciences
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728195261
DOIs
Publication statusPublished - 10 Nov 2020
Event4th International Conference on Informatics and Computational Sciences, ICICoS 2020 - Semarang, Indonesia
Duration: 10 Nov 202011 Nov 2020

Publication series

NameICICoS 2020 - Proceeding: 4th International Conference on Informatics and Computational Sciences

Conference

Conference4th International Conference on Informatics and Computational Sciences, ICICoS 2020
CountryIndonesia
CitySemarang
Period10/11/2011/11/20

Keywords

  • Convolutional neural network
  • Diabetes mellitus
  • Iridology

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