Sistem Pengembangan Deteksi Kanker Prostat Berbasis Image Processing dengan Metode Convolutional Neural Network

Authors

  • Alimin Prodi Sistem Dan Teknologi Informasi, Institut Teknologi Yadika Pasuruan, Indonesia
  • Sigit Riyadi Teknik Informatika, STMIK Yadika , Bangil, Indonesia

DOI:

https://doi.org/10.35891/explorit.v14i2.3535

Keywords:

kanker prostat, Pengolaan Citra, Convolutional Neural Network

Abstract

In the medical field, the diagnosis of prostate cancer is done by biopsy and microscopy. This testing technique takes a long time for even a dermatologist and has its own risks during the biopsy process so that it greatly affects the results of diagnosis and healing. To overcome this problem, we need a method to detect prostate cancer automatically through microscopic images of prostate cells. In this study, the method used is Convolutional Neural Network (CNN) for the identification of prostate cancer through prostate cell images. After testing on 60 images, it was found that 4 images failed to identify. The results of this study indicate that the proposed method is able to identify prostate cancer using microscopic images of prostate cells with an accuracy rate of 93.3%.

Downloads

Download data is not yet available.

References

Chodidjah, “Aspek imunologik pada kanker prostat,†Anat. Histol. Fak. Kedokt. Univ. Islam Sultan Agung, vol. 94, no. 118, 2009.

D. Sundararajan, Digital image processing: A signal processing and algorithmic approach. 2017. doi: 10.1007/978-981-10-6113-4.

J. A. Laub, “Assessing the servant organization; Development of the Organizational Leadership Assessment (OLA) model. Dissertation Abstracts International,†Procedia - Soc. Behav. Sci., vol. 1, no. 2, 1999.

G. L. Kumar et al., “Proposal Prediction of response to NCT in breast cancer with MRI,†Comput. Med. Imaging Graph., vol. 6512, no. 1, 2013.

WHO, “Indonesia Fact Sheets,†Int. Agency Res. Cancer, vol. 256, 2019.

P. Muller, “ A review of ‘ Digital Image Processing ’ (Signal Processing Series)By Kenneth R. Castleman. ( New Jersey: Prentice-Hall Inc., 1979.) Price U.S. $ 29 95. ,†Int. J. Remote Sens., vol. 1, no. 1, 1980, doi: 10.1080/01431168008948214.

S. Usmani, D. A. T. Ahmed, A. Hamadah, and F. Al Kandari, “A rare case of Diffuse Large B-Cell Lymphoma of the Prostate on 18F-FDG PET-CT,†J. Pak. Med. Assoc., vol. 71, no. 1B, 2021.

P. Devikar, “Transfer Learning for Image Classification of various dog breeds,†Int. J. Adv. Res. Comput. Eng. Technol., vol. 5, no. 12, 2016.

D. M. Etter, “Introduction to MATLAB ® Second Edition,†pp. 1–197, 2011, [Online]. Available: http://dl.icdst.org/pdfs/files3/6c7882dfb3a85367f9c3410b72588d3a.pdf

J. Cuzick et al., “Prevention and early detection of prostate cancer,†Lancet Oncol., vol. 15, no. 11, pp. e484–e492, 2014, doi: 10.1016/S1470-2045(14)70211-6.

M. I. Rosadi, M. Lutfi, and S. Artikel, “Identifikasi Jenis Penyakit Daun Jagung Menggunakan Deep Learning Pre-Trained Model,†J. Keilmuan Dan Apl. Tek. Inform., vol. 5, no. 36, pp. 35–42, 2019, [Online]. Available: https://doi.org/10.35891/explorit

Downloads

Published

2022-12-31

Issue

Vol. 14 No. 2 (2022): Jurnal Explore IT! Edisi Desember 2022

Section

Articles

License

Lisensi Creative Commons
Jelajahi IT! dilisensikan di bawah Lisensi Internasional Creative Commons Attribution 4.0 .

How to Cite

[1]
“Sistem Pengembangan Deteksi Kanker Prostat Berbasis Image Processing dengan Metode Convolutional Neural Network”, explorit, vol. 14, no. 2, pp. 52–63, Dec. 2022, doi: 10.35891/explorit.v14i2.3535.