Classification of Eye Disorders in Children Using the CNN Xception Architecture Method for Early Detection
Klasifikasi Gangguan Mata pada Anak Menggunakan Metode CNN Arsitektur Xception untuk Deteksi Dini
DOI:
https://doi.org/10.21070/ups.9296Keywords:
Detection of eye disorders, Convolution Neural Network, XceptionAbstract
The increasing use of gadgets increases the risk of eye disorders in children, so early detection is crucial. This research focuses on the classification of three eye conditions namely crossed eyes, red eyes, and normal eyes using digital images. The method used involves the Xception architecture on a Convolutional Neural Network (CNN) to extract complex features from eye images. The data used consists of 4,500 images divided into three categories. This research compares two main training scenarios: using transfer learning without fine-tuning and with fine-tuning. The research results show that the best accuracy, precision, recall and f1-score values are found in scenarios with fine-tuning, each of which consistently reaches 96%. These results can contribute to the development of early detection models of eye disorders with high accuracy, especially in demonstrating the effectiveness of fine-tuning methods for specific medical image classification tasks.
Downloads
References
GoodStats, “209,3 Juta Orang di Indonesia Menggunakan Smartphone pada Tahun 2023,” GoodStats. Accessed: Jul. 01, 2025. [Online]. Available: https://data.goodstats.id/statistic/2093-juta-orang-di-indonesia-menggunakan-smartphone-pada-tahun-2023-cbha0
B. P. Statistik, “Proporsi Individu yang Menguasai/Memiliki Telepon Genggam Menurut Kelompok Umur (Persen),” Badan Pusat Statistik. [Online]. Available: https://www.bps.go.id/id/statistics-table/2/MTIyMiMy/proporsi-individu-yang-menguasai-memiliki-telepon-genggam-menurut-kelompok-umur.html
E. A. Mashudi and S. Monasari, “DAMPAK PENGGUNAAN GADGET TERHADAP KESEHATAN MATA DAN POSTUR TUBUH PADA ANAK USIA DINI,” J. Pendidik. Islam, vol. 6, no. 1, pp. 21–28, 2024.
G. Heiting and S. Kelley, “Blue Light Facts: Is Blue Light Bad For Your Eyes?,” All About Vision. Accessed: Jun. 18, 2025. [Online]. Available: https://www.allaboutvision.com/conditions/computer-vision-syndrome/blue-light/overview-of-blue-light/
E. Van Hoolst et al., “Association between near viewing and acute acquired esotropia in children during tablet and smartphone use,” Strabismus, vol. 30, no. 2, pp. 59–64, Apr. 2022, doi: 10.1080/09273972.2022.2046113.
D. C. Agustin, M. A. Rosid, and N. Ariyanti, “Implementasi Convolutional Neural Network Untuk Deteksi Kesegaran Pada Apel,” J. Fasilkom, vol. 13, no. 02, pp. 145–150, 2023, doi: 10.37859/jf.v13i02.5175.
G. Rahguna Putri, M. Akbar Maulana, and T. Lestari, “Identifikasi Mata Juling dan Mata Normal Pada Anak Dengan Metode Convolutional Neural Network (CNN),” Just IT J. Sist. Informasi, Teknol. Inf. dan Komput., vol. 13, no. 2, pp. 80–86, 2023, [Online]. Available: https://jurnal.umj.ac.id/index.php/just-it/index
K. Tanvir, A. I. Jony, D. M. K. Haq, D. F. Nazera, A. P. D. M. Dass, and P. D. V. Raju, “Clinical Insights through Xception: A Multiclass Classification of Ocular Pathologies,” Artic. Tuijin Jishu/Journal Propuls. Technol., no. November, 2023, doi: 10.52783/tjjpt.v44.i4.2018.
M. I. Rasyid and L. M. Wisudawati, “Klasifikasi Hama Ulat Pada Citra Daun Sawi Berbasis Convolutional Neural Network Dengan Model Xception,” 2024.
Y. K. Bintang and H. Imaduddin, “Pengembangan Model Deep Learning Untuk Deteksi Retinopati Diabetik Menggunakan Metode Transfer Learning,” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 9, no. 3, pp. 1442–1455, 2024, doi: 10.29100/jipi.v9i3.5588.
Buyung Reza Muhammad, Andri Nugraha, Eti Suliyawati, and Risma Yulyyawati, “Hubungan Durasi Penggunaan Gadget dengan Kejadian Mata Lelah (Astenopia) pada Remaja SMAN 1 Garut,” J. Ris. Rumpun Ilmu Kesehat., vol. 4, no. 1, pp. 173–184, 2025, doi: 10.55606/jurrikes.v4i1.4463.
M. Nursyiam, R. Laela, S. I. Dewi, and P. Indonesia, “DAMPAK RADIASI GADGET TERHADAP KESEHATAN MATA REMAJA,” J. Kesehat. Masy. Indones., vol. 1, no. 2, pp. 74–78, 2024.
M. A. A. Fawwaz, K. N. Ramadhani, and F. Sthevani, “Klasifikasi Ras pada hewan peliharaan menggunakan Algoritma Convolutional Neural Network (CNN),” vol. 8, no. 1, pp. 715–730, 2020.
W. R. PERDANI, R. MAGDALENA, and N. K. CAECAR PRATIWI, “Deep Learning untuk Klasifikasi Glaukoma dengan menggunakan Arsitektur EfficientNet,” ELKOMIKA J. Tek. Energi Elektr. Tek. Telekomun. Tek. Elektron., vol. 10, no. 2, p. 322, 2022, doi: 10.26760/elkomika.v10i2.322.
I. Aryadi and A. Suhendar, “Implementasi Arsitektur Xception Dalam Menentukan Kematangan Tandan Buah Segar Kelapa Sawit,” Jutisi J. Ilm. Tek. Inform. dan Sist. Inf., vol. 13, no. 3, 2024, doi: 10.35889/jutisi.v13i3.2337.
R. Kurniawan, P. B. Wintoro, Y. Mulyani, and M. Komarudin, “Implementasi Arsitektur Xception Pada Model Machine Learning Klasifikasi Sampah Anorganik,” J. Inform. dan Tek. Elektro Terap., vol. 11, no. 2, pp. 233–236, 2023, doi: 10.23960/jitet.v11i2.3034.
Downloads
Additional Files
Posted
License
Copyright (c) 2025 UMSIDA Preprints Server
This work is licensed under a Creative Commons Attribution 4.0 International License.
