An Explainable Deep Learning Framework for Automated Classification of Ocular Diseases in a Big Data Environment
DOI:
https://doi.org/10.21015/vtse.v13i3.2228Abstract
Ocular diseases such as cataracts, glaucoma, age-related macular degeneration, and diabetic retinopathy remain major contributors to global visual impairment and blindness, where early detection is critical for effective intervention. While color fundus photography is widely used for retinal screening, manual interpretation is time-consuming and prone to error, highlighting the need for automated, accurate, and interpretable diagnostic solutions. In this study, we propose a deep learning based framework for the automated classification of ocular diseases and normal cases using medical images. The framework incorporates a comprehensive preprocessing pipeline and leverages a scalable Apache-powered big data environment for efficient feature extraction and model training on large-scale datasets. A Convolutional Neural Network (CNN) was proposed and benchmarked against state-of-the-art architectures including VGG19, ResNet50, and GoogLeNet, achieving superior performance with an accuracy of 97%, precision of 93%, recall of 97%, and F1-score of 93%. To enhance interpretability and clinical trust, Gradient-weighted Class Activation Mapping (Grad-CAM) was integrated, generating heatmaps that highlight the most discriminative retinal regions influencing predictions. The proposed approach not only achieves high diagnostic accuracy but also ensures transparency, scalability, and clinical relevance, making it a promising step toward real-world deployment of explainable AI systems in ophthalmology and broader healthcare applications.
References
D. B. Critser, J. Troyer, S. S. Whitmore, M. Mansoor, E. M. Stone, J. F. Russell, and I. C. Han, “High dynamic range image processing for retinal color fundus photography,” Ophthalmic Surgery, Lasers and Imaging Retina, vol. 55, no. 5, pp. 263–269, 2024, doi: 10.3928/23258160-20240131-01.
N. M. Dipu, S. A. Shohan, and K. M. A. Salam, “Ocular disease detection using advanced neural network based classification algorithms,” Asian Journal for Convergence in Technology (AJCT), vol. 7, no. 2, pp. 91–99, 2021.
N. Li, T. Li, C. Hu, K. Wang, and H. Kang, “A benchmark of ocular disease intelligent recognition: One shot for multi-disease detection,” in Proc. BenchCouncil Int. Symp. Bench 2020, pp. 177–193, 2021, doi: 10.1007/978-3-030-93719-0_14.
V. Tang, K. L. Choy, G. T. S. Ho, H. Y. Lam, and Y. P. Tsang, “An IoMT-based geriatric care management system for achieving smart health in nursing homes,” Industrial Management & Data Systems, vol. 119, no. 8, pp. 1819–1840, 2019, doi: 10.1108/IMDS-09-2018-0411.
Y. Wang, L. Kung, and T. A. Byrd, “Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations,” Technological Forecasting and Social Change, vol. 126, pp. 3–13, 2018, doi: 10.1016/j.techfore.2018.05.027.
A. Tandon, A. Dhir, A. K. N. Islam, and M. Mäntymäki, “Blockchain in healthcare: A systematic literature review, synthesizing framework and future research agenda,” Computers in Industry, vol. 122, p. 103290, 2020, doi: 10.1016/j.compind.2020.103290.
A. C. M. da Silveira, Á. Sobrinho, L. Dias da Silva, D. F. S. Santos, M. Nauman, and A. Perkusich, “Harnessing coloured Petri nets to enhance machine learning: A simulation-based method for healthcare and beyond,” Simulation Modelling Practice and Theory, vol. 140, p. 103080, 2025, doi: 10.1016/j.simpat.2025.103080.
S. Hameed, M. Nauman, N. Akhtar, M. A. B. Fayyaz, and R. Nawaz, “Explainable AI-driven depression detection from social media using natural language processing and black box machine learning models,” Frontiers in Artificial Intelligence, vol. 8, 2025, doi: 10.3389/frai.2025.1627078.
R. Zhang, G. Simon, and F. Yu, “Advancing Alzheimer’s research: A review of big data promises,” International Journal of Medical Informatics, vol. 106, pp. 48–56, 2017, doi: 10.1016/j.ijmedinf.2017.07.002.
B. A. Abdelhakim, B. A. Mohamed, and S. Fellaji, “Big data architecture for decision making in protocols and medications assignment,” in Proc. Mediterranean Symp. Smart City Appl., pp. 1–4, 2017, doi: 10.1145/3175628.3175638.
C. Austin and F. Kusumoto, “The application of big data in medicine: Current implications and future directions,” Journal of Interventional Cardiac Electrophysiology, vol. 47, pp. 51–59, 2016, doi: 10.1007/s10840-016-0103-7.
M. Salomi and S. A. A. Balamurugan, “Need, application and characteristics of big data analytics in healthcare—a survey,” Indian Journal of Science and Technology, vol. 9, no. 16, pp. 1–5, 2016, doi: 10.17485/ijst/2016/v9i16/92121.
M. George, A. Chacko, and S. K. Kurien, “Proactive diabetes management: Research directions,” in Proc. 20th Int. Conf. on Distributed Computing and Networking, pp. 486–491, 2019, doi: 10.1145/3288599.3288643.
P. Kaur, M. Sharma, and M. Mittal, “Big data and machine learning based secure healthcare framework,” Procedia Computer Science, vol. 132, pp. 1049–1059, 2018, doi: 10.1016/j.procs.2018.05.196.
F. Hussain, M. Nauman, A. Alghuried, A. Alhudhaif, and N. Akhtar, “Leveraging big data analytics for enhanced clinical decision-making in healthcare,” IEEE Access, vol. 11, pp. 127817–127836, 2023, doi: 10.1109/ACCESS.2023.3332030.
D. Gu, J. Li, X. Li, and C. Liang, “Visualizing the knowledge structure and evolution of big data research in healthcare informatics,” International Journal of Medical Informatics, vol. 98, pp. 22–32, 2017, doi: 10.1016/j.ijmedinf.2016.09.013.
C. Sáez and J. M. García-Gómez, “Kinematics of big biomedical data to characterize temporal variability and seasonality of data repositories: Functional data analysis of data temporal evolution over non-parametric statistical manifolds,” International Journal of Medical Informatics, vol. 119, pp. 109–124, 2018, doi: 10.1016/j.ijmedinf.2018.09.001.
M. Khalifa and I. Zabani, “Utilizing health analytics in improving the performance of healthcare services: A case study on a tertiary care hospital,” Journal of Infection and Public Health, vol. 9, no. 6, pp. 757–765, 2016, doi: 10.1016/j.jiph.2016.09.007.
A. C. M. da Silveira, Á. Sobrinho, L. Dias da Silva, D. F. S. Santos, M. Nauman, and A. Perkusich, “Harnessing coloured Petri nets to enhance machine learning: A simulation-based method for healthcare and beyond,” Simulation Modelling Practice and Theory, vol. 140, p. 103080, 2025.
Organisation for Economic Co-operation and Development (OECD), Rethinking Health System Performance Assessment. Paris, France: OECD Publishing, 2024.
M. Lee and A. White, “Data-driven healthcare: Improving triage and patient management,” Health Data Science, vol. 18, no. 1, pp. 45–56, 2021.
R. Dubey, A. Gunasekaran, S. J. Childe, C. Blome, and T. Papadopoulos, “Big data and predictive analytics and manufacturing performance: Integrating institutional theory, resource-based view and big data culture,” British Journal of Management, vol. 30, no. 2, pp. 341–361, 2019.
S. Gupta, H. Chen, B. T. Hazen, S. Kaur, and E. D. R. S. Gonzalez, “Circular economy and big data analytics: A stakeholder perspective,” Technological Forecasting and Social Change, vol. 144, pp. 466–474, 2019.
A. Murray, K. Skene, and K. Haynes, “The circular economy: An interdisciplinary exploration of the concept and application in a global context,” Journal of Business Ethics, vol. 140, pp. 369–380, 2017.
S. Shamim, J. Zeng, Z. Khan, and N. U. Zia, “Big data analytics capability and decision making performance in emerging market firms: The role of contractual and relational governance mechanisms,” Technological Forecasting and Social Change, vol. 161, p. 120315, 2020.
R. Rialti, L. Zollo, A. Ferraris, and I. Alon, “Big data analytics capabilities and performance: Evidence from a moderated multi-mediation model,” Technological Forecasting and Social Change, vol. 149, p. 119781, 2019.
M. Nauman, N. Akhtar, A. Alhudhaif, and A. Alothaim, “Guaranteeing correctness of machine learning based decision making at higher educational institutions,” IEEE Access, vol. 9, pp. 92864–92880, 2021.
M. Fernandes, S. M. Vieira, F. Leite, C. Palos, S. Finkelstein, and J. M. C. Sousa, “Clinical decision support systems for triage in the emergency department using intelligent systems: A review,” Artificial Intelligence in Medicine, vol. 102, p. 101762, 2020.
Paubox, “Deep learning in healthcare,” Paubox Blog, Jan. 27, 2025.
M. Nauman et al., “The role of big data analytics in revolutionizing diabetes management and healthcare decision-making,” IEEE Access, vol. 13, pp. 10767–10785, 2025.
J. Smolander, M. Dehmer, and F. Emmert-Streib, “Comparing deep belief networks with support vector machines for classifying gene expression data from complex disorders,” FEBS Open Bio, vol. 9, no. 7, pp. 1232–1248, 2019.
K. N. Alam et al., “[Retracted] Deep learning-based sentiment analysis of COVID-19 vaccination responses from Twitter data,” Computational and Mathematical Methods in Medicine, vol. 2021, no. 1, p. 4321131, 2021.
J. He, C. Li, J. Ye, Y. Qiao, and L. Gu, “Self-speculation of clinical features based on knowledge distillation for accurate ocular disease classification,” Biomedical Signal Processing and Control, vol. 67, p. 102491, 2021.
M. Nauman et al., “Improving the correctness of medical diagnostics based on machine learning with coloured Petri nets,” IEEE Access, vol. 9, pp. 143434–143447, 2021.
N. Khan et al., “Guaranteeing correctness in black-box machine learning: A fusion of explainable AI and formal methods for healthcare decision-making,” IEEE Access, vol. 12, pp. 90299–90316, 2024.
H. M. U. Akhtar et al., “Mitigating cyber threats: Machine learning and explainable AI for phishing detection,” VFAST Transactions on Software Engineering, vol. 13, no. 2, pp. 170–195, Jun. 2025. Available: https://vfast.org/journals/index.php/VTSE/article/view/2129
K. N. Alam and M. M. Khan, “CNN based COVID-19 prediction from chest X-ray images,” in Proc. IEEE UEMCON, pp. 486–492, 2021.
A. G. Roy et al., “ReLayNet: Retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks,” Biomedical Optics Express, vol. 8, no. 8, pp. 3627–3642, 2017.
S. P. K. Karri, D. Sheet, and J. Chatterjee, “Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration,” Biomedical Optics Express, vol. 8, no. 2, pp. 579–592, 2017.
M. Oda, T. Yamaguchi, H. Fukuoka, Y. Ueno, and K. Mori, “Automated eye disease classification method from anterior eye image using anatomical structure focused image classification technique,” in Proc. Medical Imaging 2020: Computer-Aided Diagnosis, vol. 11314, pp. 991–996, SPIE, 2020.
R. R. Selvaraju, M. Cogswell, A. Das, R. Mohiuddin, D. Batra, D. Parikh, and D. Krishnan, “Grad-CAM: Visual explanations from deep networks via gradient-based localization,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), pp. 618–626, 2017.
MathWorks, “Grad-CAM reveals the why behind deep learning decisions,” Online Resource. Available: https://www.mathworks.com/help/deeplearning/ug/gradcam-explains-why.html
. Accessed: Sept. 29, 2025.
H. Moujahid, A. Ebrahim, O. H. Dabees, F. Zahran, and S. Naji, “Combining CNN and Grad-CAM for COVID-19 disease prediction and visual explanation,” Intelligent Automation & Soft Computing, vol. 32, no. 2, pp. 1–12, 2022.
L. Lu and M. Liang, “Deep learning-driven medical image analysis: Integrating convolutional neural networks and explainable AI techniques,” Frontiers in Materials, vol. 12, p. 1583615, 2025, doi: 10.3389/fmats.2025.1583615.
S. Y. Chen et al., “Artificial intelligence technology in ophthalmology public health and clinical practice,” Frontiers in Cell and Developmental Biology, vol. 13, p. 1576465, 2025, doi: 10.3389/fcell.2025.1576465.
V. Gulshan et al., “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA, vol. 316, no. 22, pp. 2402–2410, 2016, doi: 10.1001/jama.2016.17216.
D. S. W. Ting, A. J. Taylor, P. Liow, C. Tan, X. Wong, R. Aguilar, and C. S. Ng, “Artificial intelligence and deep learning in ophthalmology,” British Journal of Ophthalmology, vol. 103, no. 2, pp. 167–175, 2019.
D. Jameel and A. M. Abdulazeez, “Ocular disease recognition based on deep learning: A comprehensive review,” The Indonesian Journal of Computer Science, vol. 13, no. 3, pp. 101–120, 2024.
Z. A. Choudhry, H. Shahid, S. Aziz, S. Z. H. Naqvi, and M. U. Khan, “DarkNet-19 based intelligent diagnostic system for ocular diseases,” Iranian Journal of Science and Technology, Transactions of Electrical Engineering, vol. 46, no. 4, pp. 959–970, 2022, doi: 10.1007/s40998-022-00514-4.
S. R. Flaxman et al., “Global causes of blindness and distance vision impairment 1990–2020: A systematic review and meta-analysis,” The Lancet Global Health, vol. 5, no. 12, pp. e1221–e1234, 2017.
N. I. Fauzi, H. Ismail, and I. Ahmedy, “Ocular disease recognition system using convolutional neural network,” in Proc. 2024 IEEE 14th Symp. on Computer Applications and Industrial Electronics (ISCAIE), pp. 170–175, 2024.
T. R. Fricke et al., “Global prevalence of presbyopia and vision impairment from uncorrected presbyopia: Systematic review, meta-analysis, and modelling,” Ophthalmology, vol. 125, no. 10, pp. 1492–1499, 2018.
T. Inomata et al., “From bench to bedside in dry eye disease,” International Journal of Molecular Sciences, vol. 26, no. 17, p. 40943473, 2025, doi: 10.3390/ijms261740973.
T. Inomata, M. Kato, K. Suzuki, M. Kikuchi, H. Yamada, and Y. Takahashi, “Using medical big data to develop personalized medicine for dry eye disease,” Cornea, vol. 39, pp. S39–S46, 2020.
Z. D. Soh and C. Y. Cheng, “Application of big data in ophthalmology,” Taiwan Journal of Ophthalmology, vol. 13, no. 2, pp. 123–132, 2023.
Z. D. Soh, C.-Y. Cheng, et al., “Application of big data in ophthalmology,” Frontiers in Medicine, vol. 10, p. 10361443, 2023, doi: 10.3389/fmed.2023.10361443.
C. S. Lee, J. D. Brandt, and A. Y. Lee, “Big data and artificial intelligence in ophthalmology: Where are we now?,” Ophthalmology Science, vol. 1, no. 2, pp. 1–10, 2021.
E. Dritsas and M. Trigka, “AI and ML in biomedical research: Unlocking precision medicine and accelerating discoveries,” Journal of Neonatal Surgery, vol. 14, p. 1604, 2025, doi: 10.52783/jns.v14.2940.
T. Inomata et al., “From bench to bedside in dry eye disease,” International Journal of Molecular Sciences, vol. 26, no. 17, p. 40943473, 2025, doi: 10.3390/ijms261740973.
R. T. Yanagihara, M. Suzuki, H. Nakamura, T. Takada, K. Ishii, and Y. Mori, “Methodological challenges of deep learning in optical coherence tomography for retinal diseases: A review,” Translational Vision Science & Technology, vol. 9, no. 2, p. 11, 2020.
S. Y. Wang, S. Pershing, A. Y. Lee, J. Smith, and K. Chang, “Big data requirements for artificial intelligence,” Current Opinion in Ophthalmology, vol. 31, no. 5, pp. 318–323, 2020.
M. F. Chiang et al., “The 2016 American Academy of Ophthalmology IRIS® registry (Intelligent Research in Sight) database: Characteristics and methods,” Ophthalmology, vol. 125, no. 8, pp. 1143–1148, 2018.
D. W. Parke II, F. Lum, and W. L. Rich, “The IRIS® Registry: Purpose and perspectives,” Der Ophthalmologe, vol. 113, pp. 463–468, 2016.
G. Zhang, L. Chen, Y. Wang, T. Liu, J. Huang, and J. Lin, “A novel biomarker identification approach for gastric cancer using gene expression and DNA methylation dataset,” Frontiers in Genetics, vol. 12, p. 644378, 2021.
C. S. Lee et al., “Smoking is associated with higher intraocular pressure regardless of glaucoma: A retrospective study of 12.5 million patients using the Intelligent Research in Sight (IRIS®) Registry,” Ophthalmology Glaucoma, vol. 3, no. 4, pp. 253–261, 2020.
J. P. Owen et al., “Refractive outcomes after immediate sequential vs delayed sequential bilateral cataract surgery,” JAMA Ophthalmology, vol. 139, no. 8, pp. 876–885, 2021.
A. Y. Lee, J. A. Lee, M. F. Chiang, and C. S. Lee, “Exploring a structural basis for delayed rod-mediated dark adaptation in age-related macular degeneration via deep learning,” Translational Vision Science & Technology, vol. 9, no. 2, p. 62, 2020.
Milvus.io, “How does Apache Spark support big data processing?,” Online Resource. Available: https://milvus.io/ai-quickreference/how-does-apache-spark-support-big-data-processing. Accessed: Sept. 29, 2025.
S. Salloum, R. Dautov, X. Chen, P. X. Peng, and J. Z. Huang, “Big data analytics on Apache Spark,” International Journal of Data Science and Analytics, vol. 1, pp. 145–164, 2016.
Milvus.io, “How Apache Spark unifies processing of heterogeneous big data,” Online Resource. Available: https://nebius.com/blog/posts/what-is-apache-spark-and-how-can-it-help-with-llms
. Accessed: Sept. 29, 2025.
T. M. Nithya, R. Umanesan, T. Kalavathidevi, C. Selvarathi, and A. Kavitha, “Deep learning model for big data classification in Apache Spark environment,” Intelligent Automation & Soft Computing, vol. 37, no. 3, 2023.
A. B. Arrieta et al., “Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI,” Information Fusion, vol. 58, pp. 82–115, 2020.
A. Rosenfeld and A. Richardson, “Explainability in human–agent systems,” Autonomous Agents and Multi-Agent Systems, vol. 33, pp. 673–705, 2019.
Nitor Infotech, “Explainable AI in 2025: Navigating trust and agency in a dynamic landscape,” Online Resource. Available: https://www.nitorinfotech.com/blog/explainable-ai-in-2025-navigating-trust-and-agency-in-a-dynamic-landscape/. Accessed: Sept. 29, 2025.
A. Adadi and M. Berrada, “Peeking inside the black-box: A survey on explainable artificial intelligence (XAI),” IEEE Access, vol. 6, pp. 52138–52160, 2018.
U. Ehsan, P. Tambwekar, L. Chan, B. Harrison, and M. O. Riedl, “Automated rationale generation: A technique for explainable AI and its effects on human perceptions,” in Proc. 24th Int. Conf. Intelligent User Interfaces, pp. 263–274, 2019.
L. H. Gilpin et al., “Explaining explanations: An overview of interpretability of machine learning,” in Proc. 2018 IEEE 5th Int. Conf. Data Science and Advanced Analytics (DSAA), pp. 80–89, 2018.
M. T. Ribeiro, S. Singh, and C. Guestrin, “Why should I trust you? Explaining the predictions of any classifier,” in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, pp. 1135–1144, 2016.
R. Guidotti et al., “A survey of methods for explaining black box models,” ACM Computing Surveys (CSUR), vol. 51, no. 5, pp. 1–42, 2018.
S. Dash, O. Gunluk, and D. Wei, “Boolean decision rules via column generation,” in Advances in Neural Information Processing Systems, vol. 31, 2018.
D. Wei, S. Dash, T. Gao, and O. Gunluk, “Generalized linear rule models,” in Proc. Int. Conf. Machine Learning (ICML), pp. 6687–6696, 2019.
U. Ehsan et al., “The Who in XAI: How AI background shapes perceptions of AI explanations,” in Proc. CHI Conf. Human Factors in Computing Systems, pp. 1–32, 2024.
P. M. Burlina et al., “Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks,” JAMA Ophthalmology, vol. 135, no. 11, pp. 1170–1176, 2017, doi: 10.1001/jamaophthalmol.2017.3782.
N. Gour, M. Tanveer, and P. Khanna, “Challenges for ocular disease identification in the era of artificial intelligence,” Neural Computing and Applications, vol. 35, no. 31, pp. 22887–22909, 2023, doi: 10.1007/s00521-021-06770-5.
G. Sharma, V. Anand, and S. Gupta, “Harnessing the strength of ResNet50 to improve the ocular disease recognition,” in Proc. 2023 World Conf. Communication & Computing (WCONF), pp. 1–7, 2023, doi: 10.1109/WCONF58270.2023.10234986.
X. Meng, J. Smith, A. Doe, L. Johnson, and P. Lee, “Fast and effective optic disk localization based on convolutional neural network,” Neurocomputing, vol. 312, pp. 285–295, 2018.
B. V. Baiju, R. Rajan, D. Mathew, A. Thomas, and S. Iyer, “Glaucoma and diabetic retinopathy diagnosis using image mining,” in Proc. 1st Int. Conf. Computing, Communication and Control System (I3CAC 2021), Bharath University, Chennai, India, pp. 1–?, 2021.
C. S. Lee et al., “Deep-learning-based, automated segmentation of macular edema in optical coherence tomography,” Biomedical Optics Express, vol. 8, no. 7, pp. 3440–3448, 2017.
Q. H. Nguyen, S. Tran, L. Ho, D. Le, and T. Pham, “Diabetic retinopathy detection using deep learning,” in Proc. 4th Int. Conf. Machine Learning and Soft Computing, pp. 103–107, 2020.
N. Anton, M. Schmidt, L. Fischer, P. Wagner, and A. Müller, “Assessing changes in diabetic retinopathy caused by diabetes mellitus and glaucoma using support vector machines in combination with differential evolution algorithm,” Applied Sciences, vol. 11, no. 9, p. 3944, 2021.
J. Wang, L. Yang, Z. Huo, W. He, and J. Luo, “Multi-label classification of fundus images with EfficientNet,” IEEE Access, vol. 8, pp. 212499–212508, 2020, doi: 10.1109/ACCESS.2020.3030083.
J. He, L. Li, J. Ye, Y. Qiao, and L. Gu, “Multi-label ocular disease classification with a dense correlation deep neural network,” Biomedical Signal Processing and Control, vol. 63, p. 102167, 2021, doi: 10.1016/j.bspc.2020.102167.
K. Hu, X. Jiao, Y. Wang, H. Zhang, and Q. Liu, “Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function,” Neurocomputing, vol. 309, pp. 179–191, 2018, doi: 10.1016/j.neucom.2018.05.025.
M. D. M. Goldbaum and A. Hoover, “STARE dataset website,” Clemson University, Clemson, SC, USA, 1975.
Z. Li, Y. He, S. Keel, W. Meng, R. T. Chang, and M. He, “Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs,” Ophthalmology, vol. 125, no. 8, pp. 1199–1206, 2018, doi: 10.1016/j.ophtha.2017.12.018.
S. Muchuchuti and S. Viriri, “Retinal disease detection using deep learning techniques: A comprehensive review,” Journal of Imaging, vol. 9, no. 4, p. 84, 2023, doi: 10.3390/jimaging9040084.
Y. Patil, A. Shetty, Y. Kale, R. Patil, and S. Sharma, “Multiple ocular disease detection using novel ensemble models,” Multimedia Tools and Applications, vol. 83, no. 4, pp. 11957–11975, 2024, doi: 10.1007/s11042-023-16658-7.
S. Susrutha and R. P. Mathur, “Review on ocular disease recognition using deep learning,” in Proc. Int. Conf. Advancement in Computation & Computer Technologies (InCACCT), pp. 316–321, 2023.
Q. Peng, R. M. W. W. Tseng, Y.-C. Tham, C.-Y. Cheng, and T. H. Rim, “Detection of systemic diseases from ocular images using artificial intelligence: A systematic review,” Asia-Pacific Journal of Ophthalmology, vol. 11, no. 2, pp. 126–139, 2022, doi: 10.1097/APO.0000000000000510.
H. Abdelmotaal et al., “Detecting dry eye from ocular surface videos based on deep learning,” The Ocular Surface, vol. 28, pp. 90–98, 2023, doi: 10.1016/j.jtos.2023.06.003.
Y. Prajna and M. K. Nath, “Efficient blood vessel segmentation from color fundus image using deep neural network,” Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3477–3489, 2022, doi: 10.3233/JIFS-221300.
P. Elangovan and M. K. Nath, “Detection of glaucoma from fundus image using pre-trained Densenet201 model,” NIScPR-CSIR, India, 2021.
S. Paul, G. Bhatt, and A. Dey, “Insights into ocular disease recognition: A machine learning approach,” in Proc. 3rd Int. Conf. Applied Artificial Intelligence and Computing (ICAAIC), pp. 318–322, 2024.
M. T. Islam, S. A. Imran, A. Arefeen, M. Hasan, and C. Shahnaz, “Source and camera independent ophthalmic disease recognition from fundus image using neural network,” in Proc. IEEE Int. Conf. Signal Processing, Information, Communication & Systems (SPICSCON), pp. 59–63, 2019.
V. Nair, S. Suranglikar, S. Deshmukh, and Y. Gavhane, “Multi-labelled ocular disease diagnosis enforcing transfer learning,” in Proc. 55th Annu. Conf. Information Sciences and Systems (CISS), pp. 1–6, 2021.
M. Juneja et al., “Automated detection of glaucoma using deep learning convolution network (G-net),” Multimedia Tools and Applications, vol. 79, pp. 15531–15553, 2020, doi: 10.1007/s11042-020-08971-8.
B. J. Awrahman, C. Aziz Fatah, and M. Y. Hamaamin, “A review of the role and challenges of big data in healthcare informatics and analytics,” Computational Intelligence and Neuroscience, vol. 2022, no. 1, p. 5317760, 2022, doi: 10.1155/2022/5317760.
K. Batko and A. Ślęzak, “The use of big data analytics in healthcare,” Journal of Big Data, vol. 9, no. 1, p. 3, 2022, doi: 10.1186/s40537-021-00539-0.
S. Kannan et al., “Segmentation of glomeruli within trichrome images using deep learning,” Kidney International Reports, vol. 4, no. 7, pp. 955–962, 2019, doi: 10.1016/j.ekir.2019.04.008.
B. Ginley et al., “Computational segmentation and classification of diabetic glomerulosclerosis,” Journal of the American Society of Nephrology, vol. 30, no. 10, pp. 1953–1967, 2019, doi: 10.1681/ASN.2018121259.
K. Zhang et al., “Deep learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images,” Nature Biomedical Engineering, vol. 5, no. 6, pp. 533–545, 2021, doi: 10.1038/s41551-021-00756-3.
A. Sirajudeen, S. Karthikeyan, and P. Ramesh, “Detection of cataract through feature extraction by the novel angular binary pattern (NABP) and classification by kernel-based convolutional neural networks,” [Journal/Conf. not provided], 2021.
J. Benson, T. Estrada, M. Burge, and P. Soliz, “Diabetic peripheral neuropathy risk assessment using digital fundus photographs and machine learning,” in Proc. 42nd Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBC), pp. 1988–1991, 2020.
J. Chang et al., “Association of cardiovascular mortality and deep learning funduscopic atherosclerosis score derived from retinal fundus images,” American Journal of Ophthalmology, vol. 217, pp. 121–130, 2020, doi: 10.1016/j.ajo.2020.04.032.
T. H. Rim et al., “Prediction of systemic biomarkers from retinal photographs: Development and validation of deep-learning algorithms,” The Lancet Digital Health, vol. 2, no. 10, pp. e526–e536, 2020, doi: 10.1016/S2589-7500(20)30217-7.
J. Son et al., “Predicting high coronary artery calcium score from retinal fundus images with deep learning algorithms,” Translational Vision Science & Technology, vol. 9, no. 2, p. 28, 2020, doi: 10.1167/tvst.9.2.28.
G. Dai et al., “Exploring the effect of hypertension on retinal microvasculature using deep learning on East Asian population,” PLOS ONE, vol. 15, no. 3, p. e0230111, 2020, doi: 10.1371/journal.pone.0230111.
L. Zhang et al., “Prediction of hypertension, hyperglycemia and dyslipidemia from retinal fundus photographs via deep learning: A cross-sectional study of chronic diseases in central China,” PLOS ONE, vol. 15, no. 5, p. e0233166, 2020, doi: 10.1371/journal.pone.0233166.
T. Pratap and P. Kokil, “Computer-aided diagnosis of cataract using deep transfer learning,” Biomedical Signal Processing and Control, vol. 53, p. 101533, 2019, doi: 10.1016/j.bspc.2019.101533.
G. Lim et al., “Feature isolation for hypothesis testing in retinal imaging: An ischemic stroke prediction case study,” in Proc. AAAI Conf. Artificial Intelligence, vol. 33, no. 1, pp. 9510–9515, 2019, doi: 10.1609/aaai.v33i01.33019510.
H. Zhang, K. Niu, Y. Xiong, W. Yang, Z. He, and H. Song, “Automatic cataract grading methods based on deep learning,” Computer Methods and Programs in Biomedicine, vol. 182, p. 104978, 2019, doi: 10.1016/j.cmpb.2019.06.020.
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-By) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
This work is licensed under a Creative Commons Attribution License CC BY
