36
DAFTAR PUSTAKA
Ampazis, N., Dounias, G. & Jantzen, J., 2004. Pap-Smear Classification Using Efficient Second Order Neural Network Training Algorithms. Berlin, Springer, Berlin, Heidelberg, pp. 230-245.
Bora, K. et al., 2017. Automated classification of Pap smear images to detect cervical dysplasia. Computer Methods and Programs in Biomedicine, Volume 138, pp. 31- 47.
Chankong, T., Umpon, N. T. & Auephanwiriyakul, S., 2014. Automatic cervical cell segmentation and classification in Pap smears. Computer Methods and Programs in Biomedicine, 113(2), pp. 539-556.
Deepak, R. U. et al., 2015. Computer Assisted Pap Smear Analyser for Cervical Cancer Screening. Journal of Cytology & Histology.
Dipanjan Sarkar, R. B. &. T. G., 2018. Hands-On Transfer Learning with Python:
Implement advanced deep learning and neural network models using TensorFlow and Keras.. Birmingham - Mumbai: Packt Publishing Ltd.
Dong, N., Zhao, L., Wu, C. H. & Chang, J. F., 2020. Inception v3 based cervical cell classification combined with artificially extracted features. Applied Soft Computing, Volume 93, pp. 1-8.
Faturrahman, M., Wasito, I., Mufidah, R. & Ghaisani, F. D., 2017. Multi feature fusion using deep belief network for automatic pap-smear cell image classification. Jakarta, IEEE.
He, K., Zhang, X., Ren, S. & Sun, J., 2016. Deep Residual Learning for Image Recognition. Las Vegas, NV, IEEE.
Hyeon, J., Choi, H.-J., Lee, B. D. & Lee, K. N., 2017. Diagnosing cervical cell images using pre-trained convolutional neural network as feature extractor. Jeju, IEEE.
Jantzen, J. & Dounias, G., 2006. Analysis of Pap-smear Image Data. Tenerife, Nature- Inspired Smart Information Systems.
37
Jantzen, J., Norup, J., Dounias, G. & Bjerregaard, B., 2005. Pap-smear Benchmark Data For Pattern Classification. Albufeira, Nature inspired Smart Information Systems (NiSIS), pp. 1-9.
Krizhevsky, A., Sutskever, I. & Hinton, G. E., 2012. ImageNet Classification with Deep Convolutional Neural Networks. Toronto, Neural Information Processing Systems Conference.
Kustiyati, S., 2007. Pap Smear. Gaster, Volume 7, pp. 115-123.
Lin, H. et al., 2019. Fine-Grained Classification of Cervical Cells Using Morphological and Appearance Based Convolutional Neural Networks. IEEE Access, Volume 7, pp.
71541 - 71549.
Lundervold, A. S. & Lundervold, A., 2019. An overview of deep learning in medical imaging focusing on MRI. Zeitschrift für Medizinische Physik, 29(2), pp. 102-127.
Marinakis, Y., Dounias, G. & Jantzen, J., 2009. Pap smear diagnosis using a hybrid intelligent scheme focusing on genetic algorithm based feature selection and nearest neighbor classification. Computers in Biology and Medicine, 39(1), pp. 69-78.
Sarkar, D., Bali, R. & Ghosh, T., 2018. Hands-On Transfer Learning with Python:
Implement advanced deep learning and neural network models using TensorFlow and Keras. First ed. Birmingham-Mumbai: Packt Publishing.
Simonyan, K. & Zisserman, A., 2015. Very Deep Convolutional Networks for Large- Scale Image Recognition. s.l., International Conference on Learning Representations.
Szegedy, C. et al., 2015. Going deeper with convolutions. Boston, MA, IEEE, pp. 1-9.
Szegedy, C. et al., 2016. Rethinking the Inception Architecture for Computer Vision. Las Vegas, NV, IEEE, pp. 2818-2826.
Zhao, L. et al., 2016. Automatic cytoplasm and nuclei segmentation for color cervical smear image using an efficient gap-search MRF. Computers in Biology and Medicine, Volume 71, pp. 46-56.
