@inproceedings{0154e2fbf9b64f788324282c11df9e31,
title = "Classification of cervical-cancer using pap-smear images: A convolutional neural network approach",
abstract = "Cervical cancer is the second most common and the fifth deadliest cancer in women. In this paper, we propose a deep learning approach for detecting cervix cancer from pap-smear images. Rather than designing and training a convolutional neural network (CNN) from the scratch, we show that we can employ a pre-trained CNN architecture as a feature extractor and use the output features as input to train a Support Vector Machine Classifier. We demonstrate the efficacy of such a new employment on the Herlev public database for single cell papsmear, whereby the experimental results show that our proposed system neatly outperforms other state of the art methods.",
keywords = "Convolutional neural network, Deep learning, Pap-smear classification",
author = "Bilal Taha and Jorge Dias and Naoufel Werghi",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017 ; Conference date: 11-07-2017 Through 13-07-2017",
year = "2017",
doi = "10.1007/978-3-319-60964-5\_23",
language = "British English",
isbn = "9783319609638",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "261--272",
editor = "Victor Gonzalez-Castro and \{Valdes Hernandez\}, Maria",
booktitle = "Medical Image Understanding and Analysis - 21st Annual Conference, MIUA 2017, Proceedings",
address = "Germany",
}
