@inproceedings{e0a4c6773f6440359a5162de8055db48,
title = "Retina disease classification based on colour fundus images using convolutional neural networks",
abstract = "This paper explores Convolutional Neural Networks (CNN) as a classifier to recognize retinal images. The dataset used in this research is public STARE color image dataset comprises of 61 × 70.46 × S3, and 31×35 pixels. The dataset is categorized into 15 classes. The experimentation shows that the CNN model can achieve 80.93 percent.",
keywords = "CNN, classification, deep learning, retina disease",
author = "Triwijoyo, {Bambang Krismono} and Yaya Heryadi and Lukas and Ahmad, {Adang S.} and Sabarguna, {Boy Subirosa} and Widodo Budiharto and Edi Abdurachman",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 International Conference on Innovative and Creative Information Technology: Computational Intelligence and IoT, ICITech 2017 ; Conference date: 02-11-2017 Through 04-11-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/INNOCIT.2017.8319141",
language = "English",
series = "Proceedings - 2017 International Conference on Innovative and Creative Information Technology: Computational Intelligence and IoT, ICITech 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--4",
booktitle = "Proceedings - 2017 International Conference on Innovative and Creative Information Technology",
address = "United States",
}