An optimized convolutional neural network using diffgrad for cataract image classification

Ely Sudarsono, Alhadi Bustamam, Patuan P. Tampubolon

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

Abstract

The deep learning method is a promising computational technique, especially for image classification problems. One of them is the Convolutional Neural Network (CNN), which is the most popular neural network model used and can understand enough the data. Although CNN is highly accurate, overfitting is a problem that frequently occurred. It could prevent it by optimizing the CNN method using diffGrad optimizer to overcome it. The proposed algorithm performance was validated using the cataract dataset. A cataract is an eye disease that has a clouding of the lens that affects the vision, and it is hard to detect at first. This research purpose is to classify the fundus image of cataract using CNN and optimize it using diffGrad optimizer. Finally, from the simulation results on the data from the Kaggle dataseis, it is shown that the proposed algorithm can classify the data into two classes. The classes are normal fundus images and cataract fundus images. Also, diffGrad optimizers can increase the accuracy of the classification.

Original languageEnglish
Title of host publicationInternational Conference on Science and Applied Science, ICSAS 2020
EditorsBudi Purnama, Dewanta Arya Nugraha, Fuad Anwar
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735440302
DOIs
Publication statusPublished - 16 Nov 2020
Event2020 International Conference on Science and Applied Science, ICSAS 2020 - Surakarta, Indonesia
Duration: 7 Jul 2020 → …

Publication series

NameAIP Conference Proceedings
Volume2296
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference2020 International Conference on Science and Applied Science, ICSAS 2020
CountryIndonesia
CitySurakarta
Period7/07/20 → …

Fingerprint Dive into the research topics of 'An optimized convolutional neural network using diffgrad for cataract image classification'. Together they form a unique fingerprint.

Cite this