Ocular Disease Detection Using Advanced Neural Network Based Classification Algorithms

  • Nadim Mahmud Dipu
  • Sifatul Alam Shohan
  • K.M.A Salam


One of the most challenging tasks for ophthalmologists is early screening and diagnosis of ocular diseases from fundus images. However, manual diagnosis of ocular diseases is difficult, time-consuming and it can be prone to errors. That is why a computer-aided automated ocular disease detection system is required for the early detection of various ocular diseases using fundus images. Due to the enhanced image classification capabilities of deep learning algorithms, such a system can finally be realized. In this study, we present four deep learning-based models for targeted ocular tumor detection. For this study, we trained the cutting-edge image classification algorithms such as Resnet-34, EfficientNet, MobileNetV2, and VGG-16 on the ODIR dataset consisting of 5000 fundus images that belong to 8 different classes. Each of these classes represents a different ocular disease. The VGG-16 model achieved an accuracy of 97.23%; the Resnet-34 model reached an accuracy of 90.85%; the MobileNetV2 model provided an accuracy of 94.32%, and the EfficientNet classification model achieved an accuracy of 93.82%. All of these models will be instrumental in building a real-time ocular disease diagnosis system.

Keywords: Ocular Disease Classification, Color Fundus Photography, Ocular Disease Detection, Convolutional Neural Networks, EfficientNet, VGG-16, Resnet-34, MobileNetV2, Transfer Learning


Download data is not yet available.


[1] Bourne, R. R., Stevens, G. A., White, R. A., Smith, J. L., Flaxman, S. R., Price, H., Jonas, J. B., Keeffe, J., Leasher, J., Naidoo, K., Pesudovs, K., Resnikoff, S., & Taylor, H. R. (2013). Causes of vision loss worldwide, 1990–2010: a systematic analysis. The Lancet Global Health, 1(6). https://doi.org/10.1016/s2214-109x(13)70113-x
[2] Sommer, A., Tielsch, J. M., Katz, J., Quigley, H. A., Gottsch, J. D., Javitt, J. C., Martone, J. F., Royall, R. M., Witt, K. A., & Ezrine, S. (1991). Racial Differences in the Cause-Specific Prevalence of Blindness in East Baltimore. New England Journal of Medicine, 325(20), 1412–1417. https://doi.org/10.1056/nejm199111143252004
[3] Congdon, N., O'Colmain, B., Klaver, C. C., Klein, R., Muñoz, B., Friedman, D. S., Kempen, J., Taylor, H. R., Mitchell, P., & Eye Diseases Prevalence Research Group (2004). Causes and prevalence of visual impairment among adults in the United States. Archives of ophthalmology (Chicago, Ill. : 1960), 122(4), 477–485.
[4] Application of Ocular Fundus Photography and Angiography. (2014). Ophthalmological Imaging and Applications, 154–175. https://doi.org/10.1201/b17026-12
[5] Rowe, S., MacLean, C. H., & Shekelle, P. G. (2004). Preventing Visual Loss From Chronic Eye Disease in Primary Care. JAMA, 291(12), 1487. https://doi.org/10.1001/jama.291.12.1487
[6] Kessel, L., Erngaard, D., Flesner, P., Andresen, J., Tendal, B., & Hjortdal, J. (2015). Cataract surgery and age‐related macular degeneration. An evidence‐based update. Acta Ophthalmologica, 93(7), 593–600. https://doi.org/10.1111/aos.12665Li, N., Li, T., Hu, C., Wang, K., &
[7] Li, N., Li, T., Hu, C., Wang, K., & Kang, H. (2021). A Benchmark of Ocular Disease Intelligent Recognition: One Shot for Multi-disease Detection. Benchmarking, Measuring, and Optimizing, 177–193. https://doi.org/10.1007/978-3-030-71058-3_11
[8] Miranda, E., Aryuni, M., & Irwansyah, E. (2016, November). A survey of medical image classification techniques. In 2016 International Conference on Information Management and Technology (ICIMTech) (pp. 56-61). IEEE.
[9] He, J., Li, C., Ye, J., Qiao, Y., & Gu, L. (2021). Multi-label ocular disease classification with a dense correlation deep neural network. Biomedical Signal Processing and Control, 63, 102167
[10] Li, C., Ye, J., He, J., Wang, S., Qiao, Y., & Gu, L. (2020, April). Dense correlation network for automated multi-label ocular disease detection with paired color fundus photographs. In 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) (pp. 1-4). IEEE
[11] Tayal, A., Gupta, J., Solanki, A., Bisht, K., Nayyar, A., & Masud, M. (2021). DL-CNN-based approach with image processing techniques for diagnosis of retinal diseases. Multimedia Systems, 1-22.
[12] Akil, M., Elloumi, Y., & Kachouri, R. (2020). Detection of retinal abnormalities in fundus image using CNN deep learning networks.
[13] Meng, X., Xi, X., Yang, L., Zhang, G., Yin, Y., Chen, X. (2018). Fast and effective optic disk localization based on convolutional neural network.Neurocomputing,,312,285–295. https://doi.org/10.1016/j.neucom.2018.05.114
[14] He, J., Li, C., Ye, J., Qiao, Y., Gu, L. (2021). Self-speculation of clinical features based on knowledge distillation for accurate ocular disease classification. Biomedical Signal Processing and Control, 67, 102491. https://doi.org/10.1016/j.bspc.2021.102491
[15] Roy, A. G., Conjeti, S., Karri, S. P., Sheet, D., Katouzian, A., Wachinger, C., Navab, N. (2017). ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks. Biomedical Optics Express, 8(8), 3627. https://doi.org/10.1364/boe.8.003627
[16] Lee, C. S., Tyring, A. J., Deruyter, N. P., Wu, Y., Rokem, A., Lee, A. Y. (2017). Deep-learning based, automated segmentation of macular edema in optical coherence tomography. Biomedical Optics Express, 8(7), 3440. https://doi.org/10.1364/boe.8.003440
[17] Playout, C., Duval, R., Cheriet, F. (2019). A Novel Weakly Supervised Multitask Architecture for Retinal Lesions Segmentation on Fundus
[18] Images. IEEE Transactions on Medical Imaging, 38(10), 2434–2444. https://doi.org/10.1109/tmi.2019.2906319
[19] Hu, K., Zhang, Z., Niu, X., Zhang, Y., Cao, C., Xiao, F., Gao, X. (2018). Retinal vessel segmentation of color fundus im-ages using multiscale convolutional neural network with an im-proved cross-entropy loss function. Neurocomputing, 309, 179–191. https://doi.org/10.1016/j.neucom.2018.05.011
[20] M. D. M. Goldbaum, STARE Dataset Website, Clemson University, Clemson, SC, USA, 1975
0 Views | 0 Downloads
How to Cite
Dipu, N. M., Shohan, S. A., & Salam, K. (2021). Ocular Disease Detection Using Advanced Neural Network Based Classification Algorithms. Asian Journal For Convergence In Technology (AJCT) ISSN -2350-1146, 7(2), 91-99. https://doi.org/10.33130/AJCT.2021v07i02.019