Klasifikasi Jenis Kanker Prostat Melalui Citra MRI Menggunakan Pengolahan Citra Digital
DOI:
https://doi.org/10.26905/jtmi.v10i2.12666Abstract
The prostate gland is one of the parts of the male reproductive system. The prostate gland is one of the organs that is not infrequently affected by cancer. Prostate cancer is one of the top diseases that often appears as one of the deadly diseases in the world. Including in Asia, the incidence of prostate cancer patients averages 7.21 per 100,000 men each year. To identify the symptoms of cancer, early detection in men can usually be done through a rectal examination. However, there is another method that utilizes imaging technology, specifically MRI images for prostate cancer, to determine the size of the cancer. By applying image processing methods such as Watershed segmentation and the Multiclass Support Vector Machine method, it is possible to classify the type of prostate cancer through MRI images. From the research conducted, it can be explained that the segmentation results of MRI images for prostate cancer using the Watershed method can show the detected cancer area spots. Meanwhile, the use of the MultiSVM method for classification shows an accuracy result of 90.166% for the polynomial kernel type.
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A. Muzakir, A. Desiani, and A. Amran. (2023). Klasifikasi Penyakit Kanker Prostat Menggunakan Algoritma Naive Bayes dan K-Nearest Neighbor, Komputika J. Sist. Komput., vol. 12, no. 1, pp. 73-79, 2023, doi: 10.34010/komputika.v12i1.9629.
I. A. U. Indonesia. (2022). Panduan Penanganan Kanker Prostat. Ikatan Ahli Urologi Indnesia.
C. L. Daniswara. (2020). Pencitraan Kanker Prostat, Cdk-283, vol. 47, no. 2, pp. 144-148.
B. M. Rashed and N. Popescu. (2022) Critical Analysis of the Current Medical Image-Based Processing Techniques for Automatic Disease Evaluation: Systematic Literature Review, Sensors, vol. 22, no. 18, doi: 10.3390/s22187065.
S. Saifullah. (2020). Segmentasi Citra Menggunakan Metode Watershed Transform Berdasarkan Image Enhancement Dalam Mendeteksi Embrio Telur, Syst. Inf. Syst. Informatics J., vol. 5, no. 2, pp. 53-60, doi: 10.29080/systemic.v5i2.798.
Y. Lu, Z. Jiang, T. Zhou, and S. Fu. (2019). An Improved Watershed Segmentation Algorithm of Medical Tumor Image, IOP Conf. Ser. Mater. Sci. Eng., vol. 677, no. 4, doi: 10.1088/1757-899X/677/4/042028.
R. L. Hasanah and D. Riana. (2021). Classification of Dermoscopic Image of Skin Cancer Using the GLCM Method and Multi-SVM Algorithm, Rekayasa, vol. 14, no. 3, pp. 407-415, doi: 10.21107/rekayasa.v14i3.12213.
R. Khan and T. Mehmood. (2022). Classification of Thoracic Diseases Based on Chest X-ray Images Using Kernel Support Vector Machine, Math. Probl. Eng., vol. 2022, doi: 10.1155/2022/9457730.
H. Nugroho, M. Hakimah, and A. W. Azinar, Image Enhancement Citra Zoom dengan Menggunakan Metode Bilinear Interpolation dari Kamera Webcam, Netw. Eng. Res. Oper., vol. 4, no. 2, 2019, doi: 10.21107/nero.v4i2.125.
R. Riries et al. (2021). Cervical single cell of squamous intraepithelial lesion classification using shape features and extreme learning machine, J. Phys. Conf. Ser., vol. 1816, no. 1, doi: 10.1088/1742-6596/1816/1/012081.
I. P. Wardhani et al. (2021). Algoritma Identi kasi Ciri Citra Pegunungan dengan Metode Cropping Pendahuluan Metode Penelitian, vol. 20, pp. 283-289.
R. Putra. (2022). Identifikasi Jenis Tanaman Anggrek Melalui Tekstur Bunga dengan Tapis, JOINTECS, vol. 3, no. 28, pp. 29-34.
C. Purnama Yanti and I. G. Andika. (2020). HSV Image Classification of Ancient Script on Copper Kintamani Inscriptions Using GLRCM and SVM, J. Teknol. dan Sist. Komput., vol. 8, no. 2, pp. 94-99, doi: 10.14710/jtsiskom.8.2.2020.94-99.
A. Andreansyah, R. F. Gusa, and M. Jumnahdi. (2019). Pengenalan Pola Sidik Jari Menggunakan Multi-Class Support Vector Machine, J. ELKHA, vol. 11, no. 2, pp. 79-84.
C. Wijaya, H. Irsyad, and W. Widhiarso. (2020). Klasifikasi Pneumonia Menggunakan Metode K-Nearest Neighbor Dengan Ekstraksi Glcm, J. Algoritm., vol. 1, no. 1, pp. 33-44, doi: 10.35957/algoritme.v1i1.431.
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