Klasifikasi Glaukoma Menggunakan Artificial Neural Network

Alva Rischa Qhisthana Pratika, Rita Magdalena, R Yunendah Nur Fuadah

Abstract


Abstract

 

Glaucoma is an eye disease caused by increased eyeball pressure resulting in damage to the optic nerve and the second leading cause of blindness after cataracts. Nerve damage often occurs without symptoms so that an early examination can reduce the risk of glaucoma. Therefore, the authors designed a glaucoma detection system through eye fundal images that can facilitate the detection of glaucomaby extracting various features like Rim to Disc Ratio, Cup to Disc Ratio (CDR), Vertical Cup to Disc Ratio (VCDR), Horizontal Cup to Disc Ratio (HCDR), and Horizontal to Vertical CDR (H-V CDR) with Morphological Operations dan Thresholding for segmentation of Optic Disc (OD) and Optic Cup (OC). Artificial Neural Network (ANN) is used as a classifier of glaucoma. Through this method, the test data can be divided into two classifications namely normal eyes and glaucoma eyes. 62 pieces of data will be trained and 62 pieces of data will be tested. The results obtained aim to facilitate early detection of glaucoma eyes. Accuracy on training data reaches 100% and accuracy in this study is reached 93.5484%.

Keyword: Glaucoma, Morphological Operation, Thresholding, Artificial Neural Network

 

Abstrak

Glaukoma adalah penyakit mata yang disebabkan oleh peningkatan tekanan bola mata sehingga terjadi kerusakan saraf optik dan dapat menyebabkan kebutaan nomor dua setelah katarak. Kerusakan saraf sering terjadi tanpa gejala sehingga pemeriksaan dini dapat mengurangi resiko dari glaukoma. Oleh karena itu, penulis merancang suatu sistem untuk mendeteksi glaukoma melalui citra fundus mata dengan mengekstraksi beberapa fitur yaitu Rim to Disc Ratio, Cup to Disc Ratio (CDR), Vertical Cup to Disc Ratio (VCDR), Horizontal Cup to Disc Ratio (HCDR), dan Horizontal to Vertical CDR (H-V CDR) dengan mengsegmentasi Optic Disc (OD) dan Optic Cup (OC) dengan menggunakan metode Morphological Operations dan Thresholding. Artificial Neural Network (ANN) digunakan sebagai metode klasifikasi glaukoma. Melalui metode tersebut, data uji dapat dibagi dalam dua klasifikasi yaitu mata normal dan mata glaukoma. Data latih yang akan diambil sebanyak 62 buah dan data uji yang akan diambil sebanyak 62 buah. Hasil yang diperoleh bertujuan untuk memudahkan mendeteksi secara dini mata glaukoma. Akurasi pada data latih mencapai 100% dan akurasi pada data uji mencapai 93,5484%.

Kata kunci: Glaukoma, Morphological Operation, Thresholding, Artificial Neural Network


Keywords


Glaucoma; Morphological Operation; Thresholding; Artificial Neural Network

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References


S. M. Nikam and C. Y. Patil, “Glaucoma detection from fundus images using MATLAB GUI,” Proc. - 2017 3rd Int. Conf. Adv. Comput. Commun. Autom. (Fall), ICACCA 2017, pp. 1–4, 2018.

N. N. Osborne, Glaucoma: An Open-Window to Neurodegeneration and Neuroprotection. 2008.

Y. Tham et al., “Global Prevalence of Glaucoma and Projections of Glaucoma Burden through 2040 A Systematic Review and Meta-Analysis,” Ophthalmology, vol. 121, no. 11, pp. 2081–2090, 2020.

R. Munarto, E. Permata, and I. G. A. T, “Klasifikasi Glaucoma Menggunakan Cup-To-Disc Ratio Dan Neural Network,” Simp. Nas. RAPI XV - 2016 FT UMS, pp. 370–378, 2016.

K. Choudhary, “ANN Glaucoma Detection using Cup-to-Disk Ratio and Neuroretinal Rim,” Int. J. Comput. Appl. (0975 – 8887), vol. 111, no. 11, pp. 8–14, 2015.

M. Lotankar, K. Noronha, and J. Koti, “Detection of optic disc and cup from color retinal images for automated diagnosis of glaucoma,” 2015 IEEE UP Sect. Conf. Electr. Comput. Electron. UPCON 2015, 2016.

W. Ruengkitpinyo, P. Vejjanugraha, W. Kongprawechnon, T. Kondo, P. Bunnun, and H. Kaneko, “An automatic glaucoma screening algorithm using cup-to-disc ratio and ISNT rule with support vector machine,” in IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society, 2015, pp. 000517–000521.

A. Agarwal, S. Gulia, S. Chaudhary, M. K. Dutta, C. M. Travieso, and J. B. Alonso-Hernandez, “A novel approach to detect glaucoma in retinal fundus images using cup-disk and rim-disk ratio,” IWOBI 2015 - 2015 Int. Work Conf. Bio-Inspired Intell. Intell. Syst. Biodivers. Conserv. Proc., pp. 139–144, 2015.

S. Vlad, S. Demea, H. Demea, and R. Holonec, “Neural network classifier for glaucoma diagnosis,” 2015 E-Health Bioeng. Conf. EHB 2015, pp. 1–4, 2016.

T. M. Gayathri Devi, S. Sudha, and P. Suraj, “Artificial neural networks in retinal image analysis,” 2015 3rd Int. Conf. Signal Process. Commun. Networking, ICSCN 2015, 2015.

A. Soltani, T. Battikh, I. Jabri, Y. Mlouhi, and M. N. Lakhoua, “Study of contour detection methods as applied on optic nerve’s images for glaucoma diagnosis,” Int. Conf. Control. Decis. Inf. Technol. CoDIT 2016, pp. 83–87, 2016.

R. Bock, J. Meier, L. G. Nyúl, J. Hornegger, and G. Michelson, “Glaucoma risk index:Automated glaucoma detection from color fundus images,” Med. Image Anal., vol. 14, no. 3, pp. 471–481, Jun. 2010.

H. Ahmad, A. Yamin, A. Shakeel, S. O. Gillani, and U. Ansari, “Detection of Glaucoma Using Retinal Fundus Images,” 2014 Int. Conf. Robot. Emerg. Allied Technol. Eng., pp. 321–324, 2014.

M. S. Haleem et al., “A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation to Aid Glaucoma Diagnosis,” J. Med. Syst., vol. 42, no. 1, 2018.

P. Das, S. R. Nirmala, and J. P. Medhi, “Detection of glaucoma using neuroretinal Rim information,” 2016 Int. Conf. Access. to Digit. World, ICADW 2016 - Proc., pp. 181–186, 2017.

D. Putra, Pengolahan Citra Digital - Darma Putra - Google Books. C.V Andi Offset, 2010.

R. C. Gonzalez, R. E. Woods, and P. Hall, Digital Image Processing. Prentice Hall, 2002.

S. J. Sangwine, “Colour in image processing,” Electron. Commun. Eng. J., no. October, pp. 211–219, 2000.

T. Kumar and K. Verma, “A Theory Based on Conversion of RGB image to Gray image A Theory Based on Conversion of RGB image to Gray image,” Int. J. Comput. Appl. (0975 – 8887), vol. 7, no. April 2016, pp. 6–10, 2010.

I. Onur and A. Celebi, “IMAGE HISTOGRAM EQUALIZER HARDWARE,” Proc. Acad. Int. Conf. Istanbul, Turkey, no. October, pp. 6–10, 2017.

U. Membantu and P. Mikroaneurisma, “Segmentasi citra retina digital retinopati diabetes untuk membantu pendeteksian mikroaneurisma 1),” Teknol. Elektro, vol. 9, no. 1, 2010.

D. M. Ra, I. Setiawan, W. Dewanta, H. A. Nugroho, and H. Supriyono, “Pengolah Citra Dengan Metode Thresholding,” J. Media Infotama, vol. 15, no. 2, 2019.

A. T. R. I. Utami, P. S. Informatika, F. Komunikasi, D. A. N. Informatika, and U. M. Surakarta,“Implementasi metode otsu thresholding untuk segmentasi citra daun,” 2017.

J. Rogowska, Overview and Fundamentals of Medical Image Segmentation. Academic Press.

F. Fahrianto, A. Agusta, and A. T. Muharam, “PENDETEKSIAN POSISI PLAT NOMOR MOBIL MENGGUNAKAN METODE MORFOLOGI DENGAN OPERASI DILASI, FILLING HOLES, DAN OPENING,” J. Tek. Inform., vol. 8, no. 1, pp. 10–15, 2015.

E. Putri, “PENGUJIAN CITRA JERUK BABY UNTUK MENGETAHUI AREA CACAT MENGGUNAKAN KLASIFIKASI PIXEL,” J. Nas. Pendidik. Tek. Inform., vol. 7, pp. 73–79, 2018.

S. H. Anwariningsih, A. Z. Arifin, and A. Yuniarti, “Estimasi bentuk,” vol. 5, no. 3, pp. 157–165, 2010.

R. Srisha and A. Khan, “Morphological Operations for Image Processing : Understanding and its Applications,” no. December, 2013.

S. Zahrah, R. Saptono, and E. Suryani, “Identifikasi Gejala Penyakit Padi Menggunakan Operasi Morfologi Citra,” no. Snik, pp. 100–106, 2016.

B. R. Suteja, “Penerapan Jaringan Saraf Tiruan Propagasi Balik Studi Kasus Pengenalan Jenis Kopi,” J. Inform., vol. 3, no. 1, pp. 49–62, 2007.

J. Larsen, “Introduction to Artificial Neural Networks,” in Department Of Mathematical Modelling Technical University Of Denmark, no. November, 1999.

Z. F. M. Ramli, I. Wijayanto, and S. Hadiyoso, “DETEKSI KONDISI KONSENTRASI BERDASARKAN SINYAL EEG DENGAN STIMULASI MENGHAFAL Al-QURAN DETECTION OF CONCENRATION CONDITIONS BASED ON EEG SIGNALS WITH THE STIMULATION OF AL-QURAN RECITATION Prodi S1 Teknik Telekomunikasi , Fakultas Teknik Elektro , Univers,” e- Proceeding Eng., vol. 5, no. 3, pp. 4683–4690, 2018.




DOI: http://dx.doi.org/10.22441/fifo.2020.v12i2.007

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