Myopia Identification by Fundus Photo Image Classification Using Convolutional Neural Network
DOI:
https://doi.org/10.30871/jaic.v9i5.10624Keywords:
CNN, Classification, Deep Learning, EfficientNet-B0, MyopiaAbstract
Myopia is a significant vision problem worldwide, requiring early detection to prevent further damage. This study aims to develop an image classification model using a Convolutional Neural Network (CNN) to identify myopia based on fundus images. The dataset used was 124,749 fundus images, divided into 80% for training and 20% for testing. The applied architecture was EfficientNetB0, chosen for its ability to achieve high performance with efficient computation. Experimental results showed that this model successfully achieved a classification accuracy of 97% in distinguishing between myopic and non-myopic images. These findings demonstrate the potential of CNN, especially EfficientNetB0, as a diagnostic tool for automatic myopia identification, which can accelerate the detection process and improve the accuracy of clinical diagnosis.
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[1] J. Ilmiah Kesehatan Sandi Husada, S. Faktor Risiko Kelainan Miopia Di Rumah Sakit Pertamina Bintang Amin, T. Lestari, T. Triwahyuni, and R. Syuhada, “Risk Factors for Myopia Abnormalities at the Bintang Amin Pertamina Hospital,” Juni, vol. 11, no. 1, pp. 305–312, 2020, doi: 10.35816/jiskh.v10i2.275.
[2] A. Sufina Ginting and E. Suhaymi, “Artikel Penelitian Dampak Penggunaan Gadget Terhadap Kejadian Miopia Siswa-Siswi Madrasah Aliyah Negeri 2 Langkat Tahun 2022,” vol. 5, 2024, [Online]. Available: https://jurnal.umsu.ac.id/index.php/JPH
[3] X. Zou et al., “Relationship between dry eye disease and myopia: A systematic review and meta-analysis,” Heliyon, vol. 10, no. 19, Oct. 2024, doi: 10.1016/j.heliyon.2024.e38674.
[4] A. Saputra, F. Doringin, M. Wahyu Budiana, A. Refraksi Optisi, and O. Gapopin, “Gambaran Status Refraksi Di Sekolah Smk Kesehatan Letris Indonesia 2 Kota Tangerang Selatan.”
[5] I. S. Primadiani, F. L. Rahmi, S. P. Ilmu, K. Mata, and J. H. Soedarto, “Faktor-Faktor Yang Mempengaruhi Progresivitas Miopia Pada Mahasiswa Kedokteran,” vol. 6, no. 4, pp. 1505–1517, 2017.
[6] S. Atika Salsabila et al., “Literature Review: Hubungan Antara Faktor Genetik, Gaya Hidup, dan Prevalensi Miopia.”
[7] R. Putri Zahra Lubis and M. Sarirah, “Risiko Miopia Terhadap Jarak Pandang Dekat Pada Mahasiswa Fakultas Kedokteran Universitas Muhammadiyah Sumatera Utara,” vol. 5, 2024, [Online]. Available: https://jurnal.umsu.ac.id/index.php/JPH
[8] N. Puspitasari et al., “Gambaran Kelainan Refraksi Pada Siswa/I Di Sdn Padaulun Kecamatan Majalaya Tahun 2024.”
[9] M. Marlina Simarmata, F. Doringin, N. Aulia Khofifah, and Z. Rahma Aulia, “Upaya Pencegahan Miopia Pada Anak-Anak Sekolah Dalam Rangka Hari Kesehatan Nasional Di Jakarta Convention Center.”
[10] A. P. Sambulele, I. H. M. Najoan, and W. P. Supit, “Gambaran Kejadian Miopia pada Mahasiswa Fakultas Kedokteran Universitas Sam Ratulangi,” Medical Scope Journal, vol. 7, no. 1, pp. 85–90, Jul. 2024, doi: 10.35790/msj.v7i1.55450.
[11] E. Hartati, “Klasifikasi Penyakit Mata Menggunakan Convolutional Neural Network Model Resnet-50.”
[12] A. Sandy Wardhani, F. Tri Anggraeny, and A. Mustika Rizki, “Penerapan Model Hibrida Cnn-Knn Untuk Klasifikasi Penyakit Mata,” 2024.
[13] C. Jatmoko and H. Lestiawan, “Seminar Nasional Riset dan Inovasi Teknologi (SEMNAS RISTEK) 2024 Jakarta,” 2024.
[14] E. F. Nurona Cahya et al., “SISTEMASI: Jurnal Sistem Informasi Klasifikasi Penyakit Mata Menggunakan Convolutional Neural Network (CNN).” [Online]. Available: http://sistemasi.ftik.unisi.ac.id
[15] M. S. Qulub and S. Agustin, “Indentifikasi Penyakit Mata Dengan Klasifikasi Citra Foto Fundus Mengunakan Convolutional Neural Network (CNN),” 2024
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