KLASIFIKASI TIPE SEL NORMAL/ABNORMAL BERDASARKAN CITRA PAP-SMEAR MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK
Authors
-
Muhamad Fathurahman
Universitas YARSI
-
Rachmadhani Ajeng Nurmufti
Universitas YARSI
-
Elan Suherlan
Universitas YARSI
Keywords:
CNN, Klasifikasi, Sel, Neural Network, Pap-smearAbstract
The classification of cell types plays an essential role in monitoring the growth of cancer cells. One of the methods to determine the cancer type is to analyze the pap-smear images manually. Nevertheless, the manual analysis of pap-smear images by the expert has several limitations, such as time-consuming and prone to misdiagnosis. For reducing the risks, it requires the automatic classification of cell types based on pap-smear images. This study utilizes the convolutional neural network (CNN) architectures to automatically classify the cell type into two-class categories (normal/abnormal) based on three features. These features, such as the local binary pattern, gray level co-occurrence matrix, and shape features, are extracted from pap-smear images. This study shows the performance of CNN achieved the maximum accuracy of 99.98%, 100.0%, 99.78% in training, validation, and testing data. Our approach also outperforms the performance of the baseline methods.
Keywords : CNN, Classification, Cell, Neural Network, Pap-smear
References
Ampazis, N., Dounias, G., & Jantzen, J. (2010). Pap-Smear Classification Using Efficient Second Order Neural Network Training Algorithms, 230–245. https://doi.org/10.1007/978-3-540-24674-9_25
Bora, K., Chowdhury, M., Mahanta, L. B., Kundu, M. K., & Das, A. K. (2017). Automated classification of Pap smear images to detect cervical dysplasia. Computer Methods and Programs in Biomedicine, 138, 31–47. https://doi.org/10.1016/j.cmpb.2016.10.001
Chankong, T., Theera-Umpon, N., & Auephanwiriyakul, S. (2014). Automatic cervical cell segmentation and classification in Pap smears. Computer Methods and Programs in Biomedicine, 113(2), 539–556. https://doi.org/10.1016/j.cmpb.2013.12.012
Devi, M. A., Ravi, S., Vaishnavi, J., & Punitha, S. (2016). Classification of Cervical Cancer Using Artificial Neural Networks. Procedia Computer Science, 89, 465–472. https://doi.org/10.1016/j.procs.2016.06.105
Haralick, R. M., & Shanmugam, K. (1973). Textural Features for Image Classification. IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS, SMC-3(6), 610–621. https://doi.org/10.1109/TSMC.1973.4309314
Jantzen, J., & Dounias, G. (2006). Analysis of Pap-Smear Image Data. Proceedings of the Nature-Inspired Smart Information Systems 2nd Annual Symposium, 10.
Jantzen, J., Norup, J., Dounias, G., & Bjerregaard, B. (2005). Pap-smear Benchmark Data For Pattern Classification. Proceedings of NiSIS 2005: Nature Inspired Smart Information Systems (NiSIS), 1–9.
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-Based Learning Applied to Document Recognition. Proc. OF THE IEEE. Retrieved from http://ieeexplore.ieee.org/document/726791/#full-text-section
Liu, N., & Kan, J. ming. (2016). Improved deep belief networks and multi-feature fusion for leaf identification. Neurocomputing, 216, 460–467. https://doi.org/10.1016/j.neucom.2016.08.005
Muhamad, F., Wasito, I., Mufidah, R., & Ghaisani, F. D. (2018). Multi feature fusion using deep belief network for automatic pap-smear cell image classification. Proceedings - 2017 International Conference on Computer, Control, Informatics and Its Applications: Emerging Trends In Computational Science and Engineering, IC3INA 2017, 2018-Janua, 18–22. https://doi.org/10.1109/IC3INA.2017.8251733
Zhou, X. (2018). Understanding the Convolutional Neural Networks with Gradient Descent and Backpropagation. Journal of Physics: Conference Series, 1004(1). https://doi.org/10.1088/1742-6596/1004/1/012028
Downloads
-
Abstract views: 2891 times
Downloaded: 1038 times
PDF
Published
Issue
Section
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
