• Yunendah Nur Fu'adah Fakultas Teknik Elektro, Universitas Telkom, Indonesia
  • Sofia Saidah Fakultas Teknik Elektro, Universitas Telkom, Indonesia
  • Nidaan Khofiya Fakultas Teknik Elektro, Universitas Telkom, Indonesia
  • Rita Magdalena Fakultas Teknik Elektro, Universitas Telkom, Indonesia
  • Ibnu Da'wan Salim Fakultas Teknik Elektro, Universitas Telkom, Indonesia
Keywords: Glaucoma, Fundus Images, Image Processing, Machine learning, Convolutional Neural Network


Glaucoma is an eye disease that causes damage to the optic nerve due to increased pressure in the eyeball. Delay in diagnosis and treatment of optic nerve damage due to glaucoma can lead to permanent blindness. Thus, several studies have developed a glaucoma early detection system based on digital image processing and machine learning. This study carried out glaucoma classification based on fundus image processing using Convolutional Neural Network (CNN). The CNN architecture proposed in this study consists of three convolutional layers with output channels 8, 16, 32 sequentially and a filter size of 5×5 at each layer, followed by a pooling layer and a dropout layer at the feature extraction stage. Furthermore, a fully connected layer and softmax activation function was implemented at the classification stage to classify fundus images into normal conditions, early glaucoma, moderate glaucoma, deep glaucoma, and ocular hypertension (OHT). The total amount of fundus image data used in this study consisted of 2000 fundus images divided into 1280 training data, 320 validation data, and 400 test data. 5-fold cross-validation is implemented in the training phase to select the best model. At the testing stage, the best accuracy generated by 99%, with the precision value, recall, f-1 scores and the AUC score are close to 1. According to the system performance results obtained, the proposed model can be used as a tool for medical personnel in classifying glaucoma conditions to provide appropriate medical treatment and reduce the risk of permanent blindness due to glaucoma.


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Kemenkes RI, “Infodatin Pusat Data dan Informasi Kementrian Kesehatan RI,” Millennium Challenge Account - Indonesia. pp. 1–2, 2014. [Online]. Available:

N. V. Balkrishna, N. C. Patil, K. Y. Ramesh, D. A. Sheshrao, G. Polytechnic, and G. Polytechnic, “A Review on Detection of Glaucoma from Retinal Fundus Images Using Digital Image Processing,” Int. J. Sci. Dev. Res., vol. 4, no. 12, pp. 154–157, 2019.

T. T. Khaing, T. Ruennark, P. Aimmanee, S. Makhanov, and N. Kanchanaranya, “Glaucoma Detection in Mobile Phone Retinal Images Based on ADI-GVF Segmentation with EM initialization,” ECTI Trans. Comput. Inf. Technol., vol. 15, no. 1 SE-Research Article, pp. 134–149, Jan. 2021, doi: 10.37936/ecti-cit.2021151.227261.

O. C. Devecioglu, J. Malik, T. Ince, S. Kiranyaz, E. Atalay, and M. Gabbouj, “Real-Time Glaucoma Detection from Digital Fundus Images Using Self-ONNs,” IEEE Access, vol. 9, pp. 140031–140041, 2021, doi: 10.1109/ACCESS.2021.3118102.

M. Madhusudhan, N. Malay, S. R. Nirmala, and D. Samerendra, “Image processing techniques for glaucoma detection,” Commun. Comput. Inf. Sci., vol. 192 CCIS, no. PART 3, pp. 365–373, 2011, doi: 10.1007/978-3-642-22720-2_38.

N. Sheeba, O., George, J., Rajin, P. K., Thomas, “Glaukoma detection using artificial neural network,” Int. Work. Comput. Sci. Eng., vol. 6, pp. 158–161, 2013, doi:

R. Mahum, S. U. Rehman, O. D. Okon, A. Alabrah, T. Meraj, and H. T. Rauf, “A Novel Hybrid Approach Based on Deep CNN to Detect Glaucoma Using Fundus Imaging,” Electronics, vol. 11, no. 1, 2022, doi: 10.3390/electronics11010026.

M. Claro, L. Santos, W. Silva, F. H. Araújo, N. Moura, and A. Santana, “Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature Extraction,” CLEI Electron. J., vol. 19, pp. 4:1-4:10, Aug. 2016, doi: 10.19153/cleiej.19.2.4.

C. Burana-Anusorn, W. Kongprawechnon, T. Kondo, and S. Sintuwong, “Image Processing Techniques for Glaucoma Detection Using the Cup-to-Disc Ratio Chalinee Burana-Anusorn,” Thammasat Int. J. Sci. Technol., vol. 18, no. 1, pp. 22–34, 2020.

L. Divya and J. Jacob, “Performance analysis of glaucoma detection approaches from fundus images,” Procedia Comput. Sci., vol. 143, pp. 544–551, 2018, doi: 10.1016/j.procs.2018.10.429.

D. Vijayasekar, S. Dhivya, S. Dhanalakshmi, and Dr. S. Karthik, “Survey on Detection of Glaucoma in Fundus Image by Segmentation and Classification,” Int. J. Eng. Res., vol. V4, no. 09, pp. 529–532, 2015, doi: 10.17577/ijertv4is090657.

Y. N. Fu’adah, N. C. Pratiwi, M. A. Pramudito, and N. Ibrahim, “Convolutional Neural Network (CNN) for Automatic Skin Cancer Classification System,” IOP Conf. Ser. Mater. Sci. Eng., vol. 982, no. 1, 2020, doi: 10.1088/1757-899X/982/1/012005.

P. Gokila Brindha, M. Kavinraj, P. Manivasakam, and P. Prasanth, “Brain tumor detection from MRI images using deep learning techniques,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1055, no. 1, p. 012115, 2021, doi: 10.1088/1757-899x/1055/1/012115.

P. P. Ippolito, “Feature Extraction Techniques, Towards Data Science,” 2019. Extraction aims to reduce,the original set of features

I. B. L. M. Suta, R. S. Hartati, and Y. Divayana, “Diagnosa Tumor Otak Berdasarkan Citra MRI (Magnetic Resonance Imaging).pdf,” Maj. Ilm. Teknol. Elektro, vol. 18, pp. 149–153, 2019.

J. D. Novakovic, A. Veljovic, S. S. Ilic, Z. Papic, and M. Tomovic, “Evaluation of Classification Models in Machine Learning.pdf,” Theory Appl. Math. Comput. Sci., pp. 39–46, 2017.

Putra, A. T., Usman, K., & Saidah, S. (2021). Webinar Student Presence System Based on Regional Convolutional Neural Network Using Face Recognition. Jurnal Teknik Informatika (Jutif), 2(2), 109–118.

How to Cite
Y. Nur Fu’adah, S. Saidah, N. Khofiya, R. Magdalena, and I. Da’wan Salim, “GLAUCOMA CLASSIFICATION BASED ON FUNDUS IMAGES PROCESSING WITH CONVOLUTIONAL NEURAL NETWORK”, J. Tek. Inform. (JUTIF), vol. 3, no. 3, pp. 717-722, Jun. 2022.