@inproceedings{59509675979f4298af2097523d86ed41,
title = "Convolutional Neural Network (CNN) for gland images classification",
abstract = "An automatic detection of histopathological images has an important role in helping diagnose step. Even, for determining the status of cancer, benign or malignant A conventional way in cancer detection has infirmity like user dependency, the tendency to the incorrect identification and takes more time. Convolutional Neural Network (CNN) is one of the deep learning architecture that can accommodate automatic feature extraction and classification directly. The ability of CNN to extract a feature of an image in depth underlie our research. The research aims to classify the two statuses of cancer on gland images using CNN. The training process for six, eight and ten layers exploited on this research. The accuracy obtained up to 82.98, 81.91 and 89.36 percent for six, eight and ten layers respectively. But in the future, we need to improve the computing time.",
keywords = "CNN, deep learning, gland images",
author = "Toto Haryanto and Ito Wasito and Heru Suhartanto",
year = "2018",
month = jan,
day = "19",
doi = "10.1109/ICTS.2017.8265646",
language = "English",
series = "Proceedings of the 11th International Conference on Information and Communication Technology and System, ICTS 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "55--59",
booktitle = "Proceedings of the 11th International Conference on Information and Communication Technology and System, ICTS 2017",
address = "United States",
note = "11th International Conference on Information and Communication Technology and System, ICTS 2017 ; Conference date: 31-10-2017 Through 31-10-2017",
}