Proceedings of the International Conference on Applied Science and Technology on Engineering Science 2023 (iCAST-ES 2023)

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Classification of Pupil Turbidity Based on Convolutional Neural Network (CNN) as an Early Detection of Cataract Step Using a Smartphone

Authors
Dewi Mutiara Sari1, *, Riyanto Sigit1, Dias Agata1, Muhammad Firdaus Maulana1
1Department of Informatics and Computer Engineering, Politeknik Elektronika Negeri Surabaya, Surabaya, Indonesia
*Corresponding author. Email: dewi.mutiara@pens.ac.id
Corresponding Author
Dewi Mutiara Sari
Available Online 17 February 2024.
DOI
10.2991/978-94-6463-364-1_28How to use a DOI?
Keywords
Pupil Turbidity; Iris Detection; Object Detection; Early Detction of Catarct
Abstract

Cataracts are the number one cause of blindness in the world as well as in Indonesia according to the World Health Organization (WHO). The high cases of cataracts in Indonesia are not matched by adequate health facilities. The cataract surgery rate in Indonesia is still below the ideal cataract surgery rate. Examination using a slit lamp is carried out by professionals and special equipment, so it is relatively expensive. Prevention of cataracts can be conducted by an early detection of cataracts. This study aims to detect cataracts at an early stage by using data on the classification of pupil turbidity (turbid and normal). There are two main steps in this study, the first is iris detection using a Single Shot Multibox Detector (SSD) and then followed by pupil turbidity classification using a Convolutional Neural Network (CNN) with the MobileNet architectural model. The accuracy achieved by the system in classifying pupil turbidity is 83.3% using a dataset of 160 images of normal and cataract eyes.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Applied Science and Technology on Engineering Science 2023 (iCAST-ES 2023)
Series
Advances in Engineering Research
Publication Date
17 February 2024
ISBN
10.2991/978-94-6463-364-1_28
ISSN
2352-5401
DOI
10.2991/978-94-6463-364-1_28How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Dewi Mutiara Sari
AU  - Riyanto Sigit
AU  - Dias Agata
AU  - Muhammad Firdaus Maulana
PY  - 2024
DA  - 2024/02/17
TI  - Classification of Pupil Turbidity Based on Convolutional Neural Network (CNN) as an Early Detection of Cataract Step Using a Smartphone
BT  - Proceedings of the International Conference on Applied Science and Technology on Engineering Science 2023 (iCAST-ES 2023)
PB  - Atlantis Press
SP  - 287
EP  - 301
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-364-1_28
DO  - 10.2991/978-94-6463-364-1_28
ID  - Sari2024
ER  -