Klasifikasi Kanker Paru Paru menggunakan Naïve Bayes dengan Variasi Filter dan Ekstraksi Ciri GLCM

Mohtar Yunianto, Soeparmi Soeparmi, Cari Cari, Fuad Anwar, Delta Nur Septianingsih, Tonang Dwi Ardyanto, Resta Farits Pradana

Abstract

Telah berhasil dilakukan klasifikasi kanker paru-paru dari 120 data citra CT Scan. Pada penelitian, proses preposisi dimulai dengan variasi filtering yaitu low pass filter, median filter, dan high pass filter. Segmentasi yang digunakan yaitu Otsu Thresholding yang kemudian teksturnya akan diekstraksi menggunakan fitur Gray Level Co-occurrence Matrix (GLCM) dengan variasi arah sudut. Hasil dari ekstraksi GLCM dijadikan database yang akan menjadi dataset untuk pengklasifikasian citra menggunakan klasifikasi naïve bayes. Hasil dari penelitian dengan 12 buah variasi diperoleh hasil variasi terbaik adalah median filter dengan arah sudut GLCM 0° menunjukkan tingkat akurasi yang paling tinggi sebesar 88,33 %.

Keywords

kanker paru; otsu thresholding; gray level co-occurrence matrix (GLCM); naïve bayes.

Full Text:

PDF

References

1 Umbaugh, S.E., 2017. Digital Image Processing and Analysis: Applications with MATLAB and CVIPtools. Florida: CRC Press.

2 Putra, D. 2010. Pengolahan Citra Digital. Yogyakarta: C.V Andi Offset.

3 Nurhayati, O. D., & Susanto, A. (2008). Penerapan Metode Segmentasi Pada Analisis Citra Digital Head CT-Scan. Disertasi. Universitas Gajah Mada, Yogyakarta.

4 Rahmadewi, R. and Kurnia, R., 2016. Klasifikasi penyakit paru berdasarkan citra rontgen dengan metoda segmentasi sobel. Jurnal Nasional Teknik Elektro, 5(1), pp.7-12.

5 Gao, J., Wang, B., Wang, Z., Wang, Y. and Kong, F., 2020. A Wavelet Transform-Based Image Segmentation Method. Optik, 208, p.164123.

6 Nasser, I.M. and Abu-Naser, S.S., 2019. Lung cancer detection using artificial neural network. International Journal of Engineering and Information Systems (IJEAIS), 3(3), pp.17-23.

7 Listyalina, L., 2017. Peningkatan Kualitas Citra Foto Rontgen Sebagai Media Deteksi Kanker Paru. Respati, 12(34).

8 Trisnawati, L. and Hakim, L., 2018. Segmentasi Citra Ct Scan Lung Menggunakan Deteksi Tepi Sobel Dan Metode Distance Regularized Level Set Evolution (Drlse). Explore IT!: Jurnal Keilmuan dan Aplikasi Teknik Informatika, 10(1), pp.1-13.

9 Matsumoto, M., 2010. Band-pass ε-filter for edge enhancement and noise removal. IEICE transactions on information and systems, 93(2), pp.367-375.

10 Wimmer, G., Tamaki, T., Tischendorf, J.J., Häfner, M., Yoshida, S., Tanaka, S. and Uhl, A., 2016. Directional wavelet based features for colonic polyp classification. Medical image analysis, 31, pp.16-36.

11 Kusuma, A.W. and Ellyana, R.L., 2018. Penerapan Citra Terkompresi Pada Segmentasi Citra Menggunakan Algoritma K-Means. Jurnal Terapan Teknologi Informasi, 2(1), pp.65-74.

12 Yamunadevi, M.M. and Ranjani, S.S., 2021. Efficient segmentation of the lung carcinoma by adaptive fuzzy–GLCM (AF-GLCM) with deep learning based classification. Journal of Ambient Intelligence and Humanized Computing, 12(5), pp.4715-4725.

13 Susanto, A., Dewantoro, Z.H., Sari, C.A., Setiadi, D.R.I.M., Rachmawanto, E.H. and Mulyono, I.U.W., 2020, July. Shallot Quality Classification using HSV Color Models and Size Identification based on Naive Bayes Classifier. In Journal of Physics: Conference Series (Vol. 1577, No. 1, p. 012020). IOP Publishing.

14 Bruntha, P.M., Pandian, S.I.A., Anitha, J., Mohan, P. and Dhanasekar, S., 2020, March. Local Ternary Co-occurrence Patterns based Lung Nodules Detection. In 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS) (pp. 489-492). IEEE.

15 Sofian, J. and Laluma, R.H., 2019. Klasifikasi Hasil Citra Mri Otak Untuk Memprediksi Jenis Tumor Otak dengan Metode Image Threshold Dan GLCM Menggunakan Algoritma K-NN (Nearest Neighbor) Classifier Berbasis Web. Infotronik: Jurnal Teknologi Informasi dan Elektronika, 4(2), pp.51-56.

16 Arasi, M.A., El-Horbaty, E.S.M. and El-Sayed, A., 2018, November. Classification of Dermoscopy Images Using Naïve Bayesian and Decision Tree Techniques. In 2018 1st Annual International Conference on Information and Sciences (AiCIS) (pp. 7-12). IEEE.

17 Kalaivani, S., Chatterjee, P., Juyal, S. and Gupta, R., 2017, April. Lung cancer detection using digital image processing and artificial neural networks. In 2017 International conference of Electronics, Communication and Aerospace Technology (ICECA) (Vol. 2, pp. 100-103). IEEE.

18 Makandar, A. and Halalli, B., 2015. Image enhancement techniques using highpass and lowpass filters. International Journal of Computer Applications, 109(14), pp.12-15.

19 Sevani, A., Modi, H., Patel, S. and Patel, H., 2018. Implementation of image processing techniques for identifying different stages of lung cancer. International Journal of Applied Engineering Research, 13(8), pp.6493-6499.

20 Pratap, G.P. and Chauhan, R.P., 2016, July. Detection of Lung cancer cells using image processing techniques. In 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES) (pp. 1-6). IEEE.

21 Hussain, A. and Khunteta, A., 2020, July. Semantic Segmentation of Brain Tumor from MRI Images and SVM Classification using GLCM Features. In 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA) (pp. 38-43). IEEE.

22 Singh, S., Sharma, A. and Mittal, M., Performance Evaluation of High Pass, Low Pass and Median filter on Webcam Pictures.

23 Ganesh, P.S., Kumar, T.S., Kumar, M. and Kumar, M.S.R., 2021. Brain Tumor Detection and Classification Using Image Processing Techniques. Brain, 4(3).

24 Rulaningtyas, R. and Ain, K., 2021, February. CT scan image segmentation based on hounsfield unit values using Otsu thresholding method. In Journal of Physics: Conference Series (Vol. 1816, No. 1, p. 012080). IOP Publishing.

25 Arifin, T., 2016. Analisa Perbandingan Metode Segmentasi Citra Pada Citra Mammogram. Jurnal Informatika, 3(2).

26 Zotin, A., Hamad, Y., Simonov, K. and Kurako, M., 2019. Lung boundary detection for chest X-ray images classification based on GLCM and probabilistic neural networks. Procedia Computer Science, 159, pp.1439-1448.

27 Rasmi, R.P., 2020. Peningkatan Hasil Diagnosis Kanker Payudara Dari Hasil Citra Mammogram Menggunakan Metode Ekstraksi Ciri Dan Klasifikasi.

28 Situmorang, G.T., Widodo, A.W. and Rahman, M.A., 2019. Penerapan Metode Gray Level Cooccurence Matrix (GLCM) untuk Ekstraksi Ciri pada Telapak Tangan. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer e-ISSN, 2548, p.964X.

29 Hussain, A., and Khunteta, A., 2020. Semantic Segmentation of Brain Tumor from MRI Images and SVM Classification using GLCM Features. In 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA) (pp. 38-43). IEEE.

30 Kanagaraj, G., and Kumar, P. S. 2020. Pulmonary tumor detection by virtue of GLCM. Journal of Scientific & Industrial Research, 79, 132–134

31 Chauhan, S., More, A.,Uikey R., Malviya, P., and Moghe, A., 2017. Brain tumor detection and classification in MRI images using image and data mining. International Conference on Recent Innovations in Signal processing and Embedded Systems (RISE), 2017, pp. 223-231.

32 Afriyana, Y., Purnamasari, R., dan Patmasari, R. 2018. Deteksi Kelainan Tulang Belakang Berdasarkan Citra Medis Digital Dengan Menggunakan Gray Level Co-occurrence Matrix (GLCM) dan K-nearest Neighbor (KNN). eProceedings of Engineering, 5(3). 4675-4682.

33 Augustin, I., Hidayat, B., dan Oscandar, F. 2018. Identifikasi Jenis Kelamin Berdasarkan Teraan Gigitan Berbasis Pengolahan Citra Digital Dengan Metode Gray Level Co-Occurrence Matrix (GLCM) dan Klasifikasi Support Vector Machine (SVM). eProceedings of Engineering, 5(3). 4835-4842.

DOI: https://doi.org/10.13057/ijap.v11i2.53213

Refbacks

  • There are currently no refbacks.