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 pap-smear, whereby the experimental results show that our proposed system neatly outperforms other state of the art methods.
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Taha, B., Dias, J., Werghi, N. (2017). Classification of Cervical-Cancer Using Pap-Smear Images: A Convolutional Neural Network Approach. In: Valdés Hernández, M., González-Castro, V. (eds) Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-60964-5_23
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