Automated Diabetic Retinopathy Detection Using Deep Learning: A Comparative Analysis of VGG-16 and ResNet50
Keywords:
Diabetic Retinopathy, Deep Learning, Convolutional Neural Networks, VGG-16, Resnet50, Retinal Image Analysis
Abstract
Diabetic retinopathy (DR) is a leading cause of blindness worldwide, primarily affecting individuals with prolonged diabetes. Early detection is crucial for preventing severe vision loss, yet conventional diagnostic methods are time-intensive and require specialized expertise. This study proposes a deep learning-based automated DR classification system utilizing convolutional neural networks (CNNs), specifically VGG-16 and ResNet50 architectures. The model classifies DR into five categories: normal, mild, moderate, severe, and proliferative DR. A dataset of retinal fundus images was preprocessed and analyzed using these CNN models, with performance evaluated based on classification accuracy. The VGG-16 model achieved an accuracy of 79.99%, outperforming ResNet50, which attained 70%. The findings highlight the effectiveness of deep learning in automated DR screening, demonstrating its potential for enhancing early diagnosis and patient care. Further improvements, such as advanced preprocessing, data augmentation, and hybrid modelling, can refine the accuracy and clinical applicability of AI-driven diagnostic tools.Downloads
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References
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[2]. Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. “U-Net: Convolutional Networks for Biomedical Image Segmentation.” arXiv:1505.04597, 2015, https://arxiv.org /abs/1505.04597.
[3]. Philip, S., A. D. Fleming, K. A. Goatman, S. Fonseca, P. McNamee, G. S. Scotland, et al. “The Efficacy of Automated Disease/No Disease Grading for Diabetic Retinopathy in a Systematic Screening Program.” British Journal of Ophthalmology, vol. 91, no. 11, 2007, pp. 1512–1517.
[4]. Duncombe, J. U. “Infrared Navigation Part I: An Assessment of Feasibility.” IEEE Transactions on Electron Devices, vol. 11, no. 1, Jan. 1959, pp. 34–39.
[5]. Farokhian, F., and H. Demirel. “Blood Vessels Detection and Segmentation in Retina Using Gabor Filters.” 2013 High Capacity Optical Networks and Emerging Enabling Technologies, Magosa, 2013, pp. 104-108.
[6]. Farokhian, F., and H. Demirel. “Blood Vessels Detection and Segmentation in Retina Using Gabor Filters.” 2013 High Capacity Optical Networks and Emerging Enabling Technologies, Magosa, 2013, pp. 104-108.
[7]. Jiang, Zhexin, Hao Zhang, Yi Wang, and Seok-Bum Ko. “Retinal Blood Vessel Segmentation Using a Fully Convolutional Network with Transfer Learning.” Computerized Medical Imaging and Graphics, 2018, pp. 1–15.
[8]. Muhammad, M. F., Paolo R., Andreas H., Bunyarit U., Alicja R. R., Christopher G. O., et al. “An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation.” IEEE Transactions on Biomedical Engineering, vol. 59, no. 9, Sept. 2012, pp. 2538-2548.
[9]. Gargeya, R., and T. Leng. “Automated Identification of Diabetic Retinopathy Using Deep Learning.” Ophthalmology, vol. 124, 2017, pp. 962–969.
[10]. Saha, R., A. Chowdhury, and S. Banerjee. “Diabetic Retinopathy-Related Lesions Detection and Classification Using Machine Learning Technology.” Lecture Notes in Computer Science, 2016, pp. 734–745.
[11]. Shankar, K., E. Perumal, and R. M. Vidhyavathi. “Deep Neural Network with Moth Search Optimization Algorithm-Based Detection and Classification of Diabetic Retinopathy Images.” SN Applied Sciences, vol. 2, 2020.
[12]. American Diabetes Association. “Standards of Medical Care in Diabetes—2009.” Diabetes Care, vol. 32, Suppl. 1, 2009, pp. S13.
[13]. Ansari, Mohd Aquib, and Dushyant Kumar Singh. “Human Detection Techniques for Real-Time Surveillance: A Comprehensive Survey.” Multimedia Tools and Applications, vol. 80, no. 6, 2021, pp. 8759-8808.
[14]. Ayala, A., T. Ortiz Figueroa, B. Fernandes, and F. Cruz. “Diabetic Retinopathy Improved Detection Using Deep Learning.” Applied Sciences, vol. 11, no. 24, 2021, p. 11970.
[15]. Tiwari, Manish, et al. “Machine Learning Empowered Breast Cancer Diagnosis: Insights from Coimbra Dataset Analysis.” Recent Advances in Computer Science and Communications, 2024.
[16]. Dembla, D., A. Meshram, and A. Anooja. “Enhanced Diabetic Retinopathy Detection Through Deep Learning Ensemble Models for Early Diagnosis.” International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 15s, 2024, pp. 26–38.
[17]. Albahli, S., and G. N. Ahmad Hassan Yar. “Automated Detection of Diabetic Retinopathy Using Custom Convolutional Neural Network.” Journal of X-Ray Science and Technology, vol. 30, no. 2, 2022, pp. 275–291.
[18]. Khan, Z. F., M. Ramzan, M. Raza, M. A. Khan, K. Iqbal, T. Kim, and J. H. Cha. “Deep Convolutional Neural Networks for Accurate Classification of Gastrointestinal Tract Syndromes.” Computers, Materials & Continua, vol. 78, no. 1, 2024.
[19]. Elreedy, D., and A. F. Atiya. “A Comprehensive Analysis of Synthetic Minority Oversampling Technique (SMOTE) for Handling Class Imbalance.” Information Sciences, vol. 505, 2019, pp. 32–64.
[20]. Rai, Sandeep, et al. “Optimizing Wireless Sensor Networks: Dynamic Load Balancing Strategy with AOMDV Routing.” 2024 3rd International Conference on Power Electronics and IoT Applications in Renewable Energy and Its Control (PARC), IEEE, 2024.
Published
2025-02-18
How to Cite
Sharma, A., Wadhwa, P., & Arora, S. (2025). Automated Diabetic Retinopathy Detection Using Deep Learning: A Comparative Analysis of VGG-16 and ResNet50. International Journal of Advanced Computer Technology, 14(1), 01-11. Retrieved from https://ijact.org/index.php/ijact/article/view/154
Section
Articles
