DR-Insight: AI-Powered Early Detection from Rentinal Images

The use of Convolutional Neural Networks (CNNs) for detecting retinal diseases from retina images represents a significant advancement in ophthalmic diagnostics, offering enhanced accuracy, efficiency, and scalability. By leveraging the power of deep learning, CNNs can analyze complex patterns in retinal images, enabling early and precise detection of conditions such as diabetic retinopathy, glaucoma, and age-related macular degeneration. This technology not only improves diagnostic capabilities but also facilitates large-scale screening, remote consultations, and integration into clinical workflows, thereby expanding access to quality eye care. However, ongoing efforts are needed to address challenges related to data quality, model interpretability, bias, and integration with existing systems. As these issues are resolved, CNN-based detection systems have the potential to transform retinal disease management, offering significant benefits for patient outcomes and global health.

1.      Venkata Subbarao,J. T S Sindhu, N N S Harshitha,“Detection of Retinal Degeneration via High-Resolution Fundus Images using Deep Neural Networks”, 2023 Second International Conference on Electronics and Renewable Systems (ICEARS) | 979-8-3503-4664-0/23/$31.00 ©2023 IEEE | DOI: 10.1109/ICEARS56392.2023.10085273 2.      S. K. M, M. A. V and S. M, "Retinal Image Processing using Neural Network with Deep Leaning," 2022 6th Internat ional Conference on Intelligent Computing and Control Systems (ICICCS), pp. 1030-1036, 2022. 3.      P at el, J., Umar, S.A., “ Detection of Imagery Vowel Speech Using Deep Learning, Advances in Energy Technology, vol 766, pp. 237–247, 2022. 4.      P. Kollapudi, S. Alghamdi, N. Veeraiah, Y. Alotaibi, S. Thotakura et al., "A new method for scene classificat ion from the remote sensing images," Computers, Materials & Cont inua, vol. 72, no.1, pp. 1339– 1355, 2022. 5.      Venkata Subbarao, M., Sudheer Kumar, T., Chowdary, P.S.R., Chakravarthy, V.V.S.S.S., “ Brain T umor Classification Using Decision Tree and Neural Network Classifiers” Data Engineering and Int elligent Comput ing, Lecture Notes in Networks and Systems, vol 446 , pp. 405- 412, 2022. 6.       Caicho, J. et al., “ Diabetic Ret inopat hy: Det ect ion and Classificat ion Using AlexNet , GoogleNet and ResNet50 Convolut ional Neural Networks” Smart T echnologies, Syst ems and Applicat ions, Communications in Computer and Information Science, vol 1532, 2022 . 7.      P. Kuppusamy, S. Venkatraman, C.A. Rishikeshan, Y.C.A. Padmanabha Reddy, “Deep learning based energy efficient optimal t imetable rescheduling model for intelligent met ro t ransportat ion systems, ” Physical Communicat ion, Volume 42, 2020. 8.       Lit jens, Geert , et al. "A survey on deep learning in medical image analysis." Medical image analysis 42 (2017): 60-88. 9.      Chaki, J., Thillai Ganesh, S., Cidham, S.K., Ananda Theertan, S.: Machine learning and art ificial intelligence based Diabetes Mellitus detect ion and self-management: a systematic review. J. King Saud Univ. Comp. Info. Sci. (2020). 10.    [10] M. M. U. Islam and M. Indiramma, "Retinal Vessel Segmentation using Deep Learning – A Study," 2020 Internat ional Conference on Smart Elect ronics and Communicat ion (ICOSEC), pp. 176-182, 2020.