OPTIMIZATION OF EFFICIENTNET-B0 ARCHITECTURE TO IMPROVE THE ACCURACY OF GLAUCOMA DISEASE CLASSIFICATION
DOI:
https://doi.org/10.33480/jitk.v11i2.7140Kata Kunci:
classification, convolutional neural network, efficientnetb0 , glaucomaAbstrak
Glaucoma is a chronic eye disease that can potentially cause permanent blindness if not detected early. This study aims to improve the generalization capability and reliability of glaucoma classification by optimizing the EfficientNetB0 architecture based on a Convolutional Neural Network (CNN). Optimization was carried out by applying double dropout (0.4 and 0.3) and adding a Dense layer with 128 ReLU-activated neurons to reduce overfitting and strengthen non-linear feature representation. The dataset used consists of 1,450 fundus images (899 glaucoma and 551 normal) obtained from IEEE DataPort. Model performance evaluation was performed using accuracy, precision, recall (sensitivity), specificity, F1 score, and Area Under the Curve (AUC) metrics, complemented by confusion matrix analysis to assess overall classification performance. The results showed that the optimized EfficientNetB0 model consistently outperformed the baseline comparison model with the highest accuracy, precision, recall (sensitivity), specificity, F1 score, and AUC values of 95%. Based on the system performance results obtained, the Proposed model can be used as an aid for medical personnel in classifying glaucoma conditions so that they can provide appropriate medical treatment and reduce the risk of permanent blindness due to glaucoma.
Unduhan
Referensi
H. Zhang and S. Lee, “Robot Bionic Vision Technologies: A Review,” Appl. Sci., vol. 12, no. 8, pp. 1–28, 2022, doi: 10.3390/app12167970.
J. Wolf et al., “The Human Eye Transcriptome Atlas: A searchable comparative transcriptome database for healthy and diseased human eye tissue,” Genomics, vol. 114, no. 2, pp. 1–8, 2022, doi: 10.1016/j.ygeno.2022.110286.
L. Yi, B. Hou, H. Zhao, and X. Liu, “X-ray-to-visible light-field detection through pixelated colour conversion,” Nature, vol. 618, no. 8, pp. 281–286, 2023, doi: 10.1038/s41586-023-05978-w.
Y. Tang, S. Song, S. Gui, W. Chao, C. Cheng, and R. Qin, “Active and Low-Cost Hyperspectral Imaging for the Spectral Analysis of a Low-Light Environment,” Sensors, vol. 23, no. 3, pp. 1–16, 2023, doi: 10.3390/s23031437.
B. S. Ajgaonkar, A. Kumaran, S. Kumar, R. D. Jain, and R. D. Jain, “Cell-based Therapies for Corneal and Retinal Disorders,” Springer Nat. Link, vol. 19, no. 11, pp. 2650–2682, 2023, doi: https://doi.org/10.1007/s12015-023-10623-0.
M. Ferrara et al., “Retinal and Corneal Changes Associated with Intraocular Silicone Oil Tamponade,” J. Clin. Med., vol. 11, no. 17, pp. 1–24, 2022, doi: 10.3390/jcm11175234.
A. R. Gogliettino et al., “High-Fidelity Reproduction of Visual Signals by Electrical Stimulation in the Central Primate Retina,” J. Neurosci., vol. 43, no. 25, pp. 4625–4641, 2023, doi: 10.1523/JNEUROSCI.1091-22.2023.
K. Martínez-Palacios, S. Vásquez-García, O. A. Fariyike, C. Robba, and A. M. Rubiano, “Using Optic Nerve Sheath Diameter for Intracranial Pressure (ICP) Monitoring in Traumatic Brain Injury: A Scoping Review,” Neurocrit. Care, vol. 40, no. 3, pp. 1193–1212, 2024, doi: 10.1007/s12028-023-01884-1.
N. Jain et al., “Selectivity for food in human ventral visual cortex,” Commun. Biol., vol. 6, no. 1, pp. 1–14, 2023, doi: 10.1038/s42003-023-04546-2.
T. Hellas, N. Intelligence, S. Bio-x, H. A. Intelligence, and E. Physics, “Functional connectomics spanning multiple areas of mouse visual cortex,” Nature, vol. 640, no. 8058, pp. 435–447, 2025, doi: 10.1038/s41586-025-08790-w.
L. Cui, Y. Xiao, Z. Xiang, Z. Chen, C. Yang, and H. Zou, “Study on the correlation between iris blood flow, iris thickness and pupil diameter in the resting state and after pharmacological mydriasis in patients with diabetes mellitus,” BMC Ophthalmol., vol. 24, no. 1, pp. 1–9, 2024, doi: 10.1186/s12886-024-03322-y.
S. Ahmed, M. M. Amin, and S. Sayed, “Ocular Drug Delivery: a Comprehensive Review,” AAPS PharmSciTech, vol. 24, no. 2, pp. 1–29, 2023, doi: 10.1208/s12249-023-02516-9.
A. Kamińska, J. Pinkas, I. Wrześniewska-Wal, J. Ostrowski, and M. Jankowski, “Awareness of Common Eye Diseases and Their Risk Factors—A Nationwide Cross-Sectional Survey among Adults in Poland,” Int. J. Environ. Res. Public Health, vol. 20, no. 2, pp. 1–20, 2023, doi: 10.3390/ijerph20043594.
A. Scarabosio et al., “Thyroid Eye Disease: Advancements in Orbital and Ocular Pathology Management,” J. Pers. Med., vol. 14, no. 7, pp. 1–16, 2024, doi: 10.3390/jpm14070776.
U. Nwagbo and P. S. Bernstein, “Understanding the Roles of Very-Long-Chain Polyunsaturated Fatty Acids (VLC-PUFAs) in Eye Health,” Nutrients, vol. 15, no. 7, pp. 1–18, 2023, doi: 10.3390/nu15143096.
T. Varzakas and S. Smaoui, “Global Food Security and Sustainability Issues: The Road to 2030 from Nutrition and Sustainable Healthy Diets to Food Systems Change,” Foods, vol. 13, no. 2, pp. 1–29, 2024, doi: 10.3390/foods13020306.
C. Pinto and D. A. Soares, “Advancements in Glaucoma Diagnosis : The Role of AI in Medical Imaging,” Diagnostics, vol. 14, no. 4, pp. 1–14, 2024.
G. D. Souza, P. C. S. Mayur, and A. Pandya, “AlterNet K : a small and compact model for the detection of glaucoma,” Biomed. Eng. Lett., vol. 14, no. 1, pp. 23–33, 2024, doi: 10.1007/s13534-023-00307-6.
G. N. Patton and H. J. Lee, “Chemical Insights into Topical Agents in Intraocular Pressure Management: From Glaucoma Etiopathology to Therapeutic Approaches,” Pharmaceutics, vol. 16, no. 2, pp. 1–41, 2024, doi: 10.3390/pharmaceutics16020274.
R. Raveendran et al., “Current Innovations in Intraocular Pressure Monitoring Biosensors for Diagnosis and Treatment of Glaucoma—Novel Strategies and Future Perspectives,” Biosensors, vol. 13, no. 6, pp. 1–22, 2023, doi: 10.3390/bios13060663.
S. Fujimoto et al., “Macular Retinoschisis from Optic Disc without a Visible Optic Pit or Advanced Glaucomatous Cupping (No Optic Pit Retinoschisis [NOPIR]),” Ophthalmol. Retin., vol. 7, no. 9, pp. 811–818, 2023, doi: 10.1016/j.oret.2023.05.020.
J. R. Tribble et al., “Neuroprotection in glaucoma: Mechanisms beyond intraocular pressure lowering,” Mol. Aspects Med., vol. 92, no. 6, pp. 1–20, 2023, doi: 10.1016/j.mam.2023.101193.
N. A. Sharif, “Recently Approved Drugs for Lowering and Controlling Intraocular Pressure to Reduce Vision Loss in Ocular Hypertensive and Glaucoma Patients,” Pharmaceuticals, vol. 16, no. 6, pp. 1–35, 2023, doi: 10.3390/ph16060791.
R. Yamagishi-Kimura, M. Honjo, and M. Aihara, “Effect of a fixed combination of ripasudil and brimonidine on aqueous humor dynamics in mice,” Sci. Rep., vol. 14, no. 1, pp. 1–13, 2024, doi: 10.1038/s41598-024-58212-6.
J. M. Micheletti, M. Shultz, I. P. Singh, and T. W. Samuelson, “An Emerging Multi-mechanism and Multi-modal Approach in Interventional Glaucoma Therapy,” Ophthalmol. Ther., vol. 14, no. 1, pp. 13–22, 2024, doi: 10.1007/s40123-024-01073-z.
M. F. Cordeiro, S. Gandolfi, K. Gugleta, E. M. Normando, and F. Oddone, “How latanoprost changed glaucoma management,” Acta Ophthalmol., vol. 102, no. 2, pp. 140–155, 2024, doi: 10.1111/aos.15725.
E. Konstantakopoulou et al., “Selective Laser Trabeculoplasty After Medical Treatment for Glaucoma or Ocular Hypertension.,” JAMA Ophthalmol., vol. 143, no. 4, pp. 295–302, 2025, doi: 10.1001/jamaophthalmol.2024.6492.
T. Hussain and H. Shouno, “Explainable Deep Learning Approach for Multi-Class Brain Magnetic Resonance Imaging Tumor Classification and Localization Using Gradient-Weighted Class Activation Mapping,” Inf., vol. 14, no. 11, pp. 1–32, 2023, doi: 10.3390/info14120642.
F. Bagatin, A. Prpic, J. Skunca Herman, O. Zrinscak, R. Ivekovic, and Z. Vatavuk, “Correlation between Structural and Functional Changes in Patients with Raised Intraocular Pressure Due to Graves Orbitopathy,” Diagnostics, vol. 14, no. 6, pp. 1–13, 2024, doi: 10.3390/diagnostics14060649.
Y. Cheng, M. Yusufu, R. N. Weinreb, and N. Wang, “Dual pressure theory in pathogenesis of glaucomatous optic neuropathy from the Beijing intracranial and intraocular pressure study,” Wiley Interdiscip. Rev. Data Min. Knowl. Discov., vol. 1, no. 11, pp. 11–19, 2024, doi: 10.1002/eer3.3.
K. M. Alabdulwahhab, “Diabetic Retinopathy Screening Using Non-Mydriatic Fundus Camera in Primary Health Care Settings – A Multicenter Study from Saudi Arabia,” Int. J. Gen. Med., vol. 16, no. 6, pp. 2255–2262, 2023, doi: 10.2147/IJGM.S410197.
S. D. Browning, J. M. Costello, H. P. Dunn, and C. L. Fraser, “The Use Of Fundus Photography In The Emergency Room A Review,” Curr. Neurol. Neurosci. Rep., vol. 25, no. 1, pp. 1–12, 2025, doi: 10.1007/s11910-025-01417-7.
Z. A. T. Ahmed, E. Albalawi, T. H. H. Aldhyani, M. E. Jadhav, P. Janrao, and M. R. M. Obeidat, “Applying Eye Tracking with Deep Learning Techniques for Early-Stage Detection of Autism Spectrum Disorders,” Data, vol. 8, no. 11, pp. 1–27, 2023, doi: 10.3390/data8110168.
J. I. Lim et al., “Artificial Intelligence Detection of Diabetic Retinopathy,” Ophthalmol. Sci., vol. 3, no. 1, pp. 1–8, 2023, doi: 10.1016/j.xops.2022.100228.
M. Ilham, A. Prihantoro, I. K. Perdana, R. Magdalena, and S. Saidah, “Experimenting with the Hyperparameter of Six Models for Glaucoma Classification,” J. Ilm. Tek. Elektro Komput. dan Inform., vol. 9, no. 3, pp. 571–584, 2023, doi: 10.26555/jiteki.v9i3.26331.
M. S. Puchaicela-Lozano et al., “Deep Learning for Glaucoma Detection: R-CNN ResNet-50 and Image Segmentation,” J. Adv. Inf. Technol., vol. 14, no. 6, pp. 1186–1197, 2023, doi: 10.12720/jait.14.6.1186-1197.
Z. Arif, R. Y. Nur Fu’adah, S. Rizal, and D. Ilhamdi, “Classification of eye diseases in fundus images using Convolutional Neural Network (CNN) method with EfficientNet architecture,” JRTI (Jurnal Ris. Tindakan Indones., vol. 8, no. 1, p. 125, 2023, doi: 10.29210/30032835000.
##submission.downloads##
Diterbitkan
Cara Mengutip
Terbitan
Bagian
Lisensi
Hak Cipta (c) 2025 Imam Akbari, Dedy Hartama, Anjar Wanto
Artikel ini berlisensi Creative Commons Attribution-NonCommercial 4.0 International License.
-a.jpg)
-b.jpg)
