Design of A Cataract Detection System based on The Convolutional Neural Network
DOI:
https://doi.org/10.31961/eltikom.v8i1.1019Keywords:
cataract eye, convolutional neural network, deep learning, image processingAbstract
Cataract, a condition characterized by clouding of the eye's lens, leads to decreased vision and potentially blindness. In Indonesia, it is the predominant cause of blindness, accounting for 81.2% of cases. Given the rising life expectancy, the incidence of degenerative diseases like cataracts is expected to increase. This research aims to develop a cataract detection system capable of classifying eye images as either indicative of cataracts or normal. Utilizing Convolutional Neural Networks (CNN) and RGB-based image processing—including edge detection techniques such as Canny and Prewitt—the system identifies eye contours. This facilitates image segmentation to ascertain the eye's condition. Therefore, image collection and processing models play a crucial role in this study. The research findings indicate that the system functions effectively, with a 98% success rate in accurately processing normal eye images through the CNN model without detecting cataracts. When tested using grayscale imaging, cataract-afflicted eyes—characterized by red spots in the images—were also successfully identified by the CNN model. These test results demonstrate that the designed cataract detection system can accurately classify images into normal or cataract-afflicted eyes with high precision. This system shows promise for use in early cataract detection, potentially helping to reduce the prevalence of cataract-related blindness in Indonesia.
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A. U. Detty, I. Artini, and V. R. Yulian, “Karakteristik Faktor Risiko Penderita Katarak,” J. Ilm. Kesehat. Sandi Husada, vol. 10, no. 1, pp. 12–17, 2021.
N. M. A. Suryathi, “KATARAK : Kebutaan yang Dapat Dicegah,” Direktorat Jenderal Pelayanan Kesehatan. Accessed: Dec. 05, 2023. [Online]. Available: https://yankes.kemkes.go.id/view_artikel/891/katarak-kebutaan-yang-dapat-dicegah
N. D. F. A. Moeloe, “Jumlah Lansia Meningkat, Kasus Katarak Tertinggi Tahun 2045,” Kumparan. Accessed: Dec. 05, 2023. [Online]. Available: https://kumparan.com/beritaanaksurabaya/jumlah-lansia-meningkat-kasus-katarak-tertinggi-tahun-2045-1zd7rwjS5x2
M. Zainuddin, L. T. Sianturi, and R. K. Hondro, “Implementasi Metode Robinson Operator 3 Level Untuk Mendeteksi Tepi Pada Citra Digital,” JURIKOM (Jurnal Ris. Komputer), vol. 4, no. 4, 2017.
G. A. Wiguna, “Sistem deteksi katarak menggunakan metode ekstraksi indeks warna dengan klasifikasi jarak euklidean,” J. Pendidik. Teknol. Inf., vol. 1, no. 2, pp. 40–46, 2018.
A. S. R. M. Sinaga, “Implementasi Teknik Threshoding Pada Segmentasi Citra Digital,” J. Mantik Penusa, vol. 1, no. 2, 2017.
M. Arsal, B. A. Wardijono, and D. Anggraini, “Face Recognition Untuk Akses Pegawai Bank Menggunakan Deep Learning Dengan Metode CNN,” J. Nas. Teknol. dan Sist. Inf, vol. 6, no. 1, pp. 55–63, 2020.
B. Nugroho and E. Y. Puspaningrum, “Kinerja Metode CNN untuk Klasifikasi Pneumonia dengan Variasi Ukuran Citra Input,” J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 3, pp. 533–538, 2021.
H. A. Pratiwi, M. Cahyanti, and M. Lamsani, “Implementasi Deep Learning Flower Scanner Menggunakan Metode Convolutional Neural Network,” Sebatik, vol. 25, no. 1, pp. 124–130, 2021.
A. A. A. Paliwang, M. R. D. Septian, M. Cahyanti, and E. R. Swedia, “Klasifikasi Penyakit Tanaman Apel Dari Citra Daun Dengan Convolutional Neural Network,” Sebatik, vol. 24, no. 2, pp. 207–212, 2020.
P. Purwanto and S. Sumardi, “Perancangan Klasifikasi Tanaman Herbal Menggunakan Transfer Learning Pada Algoritma Convolutional Neural Network (CNN),” J. Ilm. Infokam, vol. 18, no. 2, pp. 105–118, 2022.
F. Zamachsari and N. Puspitasari, “Penerapan Deep Learning dalam Deteksi Penipuan Transaksi Keuangan Secara Elektronik,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 2, pp. 203–212, 2021.
D. M. Madkour, M. Ahmed, and W. F. Mohamed, “Automatic face and hijab segmentation using convolutional network,” Int. J. Integr. Eng., vol. 11, no. 7, pp. 61–66, 2019.
M. D. Darojat, Y. A. Sari, and R. C. Wihandika, “Convolutional Neural Network untuk Klasifikasi Citra Makanan Khas Indonesia,” J. Pengemb. Teknol. Inf. dan Ilmu Komput, vol. 5, no. 11, pp. 4764–4769, 2021.
H. Darmanto, “Pengenalan spesies ikan berdasarkan kontur otolith menggunakan convolutional neural network,” Joined J. (Journal Informatics Educ., vol. 2, no. 1, pp. 41–59, 2019.
D. Prasetyawan and R. Gatra, “Model Convolutional Neural Network untuk Mengukur Kepuasan Pelanggan Berdasarkan Ekspresi Wajah,” J. Tek. Inform. dan Sist. Inf., vol. 8, no. 3, pp. 661–673, 2022.
S. Rahman and H. Dafitri, “Pengembangan Convolutional Neural Network untuk Klasifikasi Ketersediaan Ruang Parkir,” Explorer (Hayward)., vol. 2, no. 1, pp. 1–6, 2022.
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