IECE Transactions on Emerging Topics in Artificial Intelligence
ISSN: 3066-1676 (Online) | ISSN: 3066-1668 (Print)
Email: [email protected]
[1] Jain, A., Jalui, A., Jasani, J., Lahoti, Y., & Karani, R. (2019, April). Deep learning for detection and severity classification of diabetic retinopathy. In 2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT) (pp. 1-6). Ieee.
[2] Rajalakshmi, R., Subashini, R., Anjana, R. M., & Mohan, V. (2018). Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence. Eye, 32(6), 1138-1144.
[3] Sanjana, S., Shadin, N. S., & Farzana, M. (2021, November). Automated diabetic retinopathy detection using transfer learning models. In 2021 5th International Conference on Electrical Engineering and Information Communication Technology (ICEEICT) (pp. 1-6). IEEE.
[4] Raj, K. (2023, October). A neuro-symbolic approach to enhance interpretability of graph neural network through the integration of external knowledge. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (pp. 5177-5180).
[5] Nair, M., & Mishra, D. S. (2019). Categorization of diabetic retinopathy severity levels of transformed images using clustering approach. Int J Comput Sci Eng, 7(1), 642-648.
[6] Neelapala, A. K., Satapathi, G. S., & Mosa, S. A. (2023, February). Severity Analysis Automation for Detection of Non-Proliferative Diabetic Retinopathy. In International Symposium on Sustainable Energy and Technological Advancements (pp. 301-312). Singapore: Springer Nature Singapore.
[7] Kropp, M., Golubnitschaja, O., Mazurakova, A., Koklesova, L., Sargheini, N., Vo, T. T. K. S., ... & Thumann, G. (2023). Diabetic retinopathy as the leading cause of blindness and early predictor of cascading complications—risks and mitigation. Epma Journal, 14(1), 21-42.
[8] Butt, M. M., Iskandar, D. A., Abdelhamid, S. E., Latif, G., & Alghazo, R. (2022). Diabetic retinopathy detection from fundus images of the eye using hybrid deep learning features. Diagnostics, 12(7), 1607.
[9] Patibandla, R. L., Rao, B. T., & Murty, M. R. (2024). Revolutionizing Diabetic Retinopathy Diagnostics and Therapy through Artificial Intelligence: A Smart Vision Initiative. In Transformative Approaches to Patient Literacy and Healthcare Innovation (pp. 136-155). IGI Global.
[10] Javed, M., Zhang, Z., Dahri, F. H., & Laghari, A. A. (2024). Real-time deepfake video detection using eye movement analysis with a hybrid deep learning approach. Electronics, 13(15), 2947.
[11] Kassani, S. H., Kassani, P. H., Khazaeinezhad, R., Wesolowski, M. J., Schneider, K. A., & Deters, R. (2019, December). Diabetic retinopathy classification using a modified xception architecture. In 2019 IEEE international symposium on signal processing and information technology (ISSPIT) (pp. 1-6). IEEE.
[12] Hill, L., & Makaroff, L. E. (2016). Early detection and timely treatment can prevent or delay diabetic retinopathy. diabetes research and clinical practice, 120, 241-243.
[13] Voets, M., Møllersen, K., & Bongo, L. A. (2019). Reproduction study using public data of: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. PloS one, 14(6), e0217541.
[14] Raj, K., & Mileo, A. (2024, September). Towards Understanding Graph Neural Networks: Functional-Semantic Activation Mapping. In International Conference on Neural-Symbolic Learning and Reasoning (pp. 98-106). Cham: Springer Nature Switzerland.
[15] Saranya, P., Umamaheswari, K. M., Sivaram, M., Jain, C., & Bagchi, D. (2021, January). Classification of different stages of diabetic retinopathy using convolutional neural networks. In 2021 2nd International Conference on Computation, Automation and Knowledge Management (ICCAKM) (pp. 59-64). IEEE.
[16] Toledo-Cortés, S., De La Pava, M., Perdómo, O., & González, F. A. (2020). Hybrid deep learning Gaussian process for diabetic retinopathy diagnosis and uncertainty quantification. In Ophthalmic Medical Image Analysis: 7th International Workshop, OMIA 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings 7 (pp. 206-215). Springer International Publishing.
[17] Gour, N., & Khanna, P. (2021). Multi-class multi-label ophthalmological disease detection using transfer learning based convolutional neural network. Biomedical signal processing and control, 66, 102329.
[18] Dugas, E., Jared, Jorge, & Cukierski, W. (2015). Diabetic Retinopathy Detection. Retrieved from https://kaggle.com/competitions/ diabetic-retinopathy-detection
[19] Karthik, Maggie, & Dane, S. (2019). APTOS 2019 Blindness Detection. Retrieved from https://kaggle.com/competitions/ aptos2019-blindness-detection
[20] Panwar, A., Semwal, G., Goel, S., & Gupta, S. (2022, April). Stratification of the lesions in color fundus images of diabetic retinopathy patients using deep learning models and machine learning classifiers. In Edge Analytics: Select Proceedings of 26th International Conference—ADCOM 2020 (pp. 653-666). Singapore: Springer Singapore.
[21] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818-2826).
[22] Masood, S., Luthra, T., Sundriyal, H., & Ahmed, M. (2017, May). Identification of diabetic retinopathy in eye images using transfer learning. In 2017 International conference on computing, communication and automation (ICCCA) (pp. 1183-1187). IEEE.
[23] Harun, N. H., Yusof, Y., Hassan, F., & Embong, Z. (2019, April). Classification of fundus images for diabetic retinopathy using artificial neural network. In 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT) (pp. 498-501). IEEE.
[24] Anwar, M., Abdullah, A. H., Altameem, A., Qureshi, K. N., Masud, F., Faheem, M., ... & Kharel, R. (2018). Green communication for wireless body area networks: energy aware link efficient routing approach. Sensors, 18(10), 3237.
[25] Zago, G. T., Andreão, R. V., Dorizzi, B., & Salles, E. O. T. (2020). Diabetic retinopathy detection using red lesion localization and convolutional neural networks. Computers in biology and medicine, 116, 103537.
[26] Saranya, P., & Prabakaran, S. (2020). Automatic detection of non-proliferative diabetic retinopathy in retinal fundus images using convolution neural network. Journal of Ambient Intelligence and Humanized Computing, 1-10.
[27] Yang, Y., Li, T., Li, W., Wu, H., Fan, W., & Zhang, W. (2017). Lesion detection and grading of diabetic retinopathy via two-stages deep convolutional neural networks. In Medical Image Computing and Computer Assisted Intervention-MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III 20 (pp. 533-540). Springer International Publishing.
[28] Hemanth, D. J., Deperlioglu, O., & Kose, U. (2020). RETRACTED ARTICLE: An enhanced diabetic retinopathy detection and classification approach using deep convolutional neural network. Neural Computing & Applications, 32(3), 707-721.
[29] Jiang, H., Yang, K., Gao, M., Zhang, D., Ma, H., & Qian, W. (2019, July). An interpretable ensemble deep learning model for diabetic retinopathy disease classification. In 2019 41st annual international conference of the IEEE engineering in medicine and biology society (EMBC) (pp. 2045-2048). IEEE.
[30] Gangwar, A. K., & Ravi, V. (2021). Diabetic retinopathy detection using transfer learning and deep learning. In Evolution in Computational Intelligence: Frontiers in Intelligent Computing: Theory and Applications (FICTA 2020), Volume 1 (pp. 679-689). Springer Singapore.
[31] Math, L., & Fatima, R. (2021). Adaptive machine learning classification for diabetic retinopathy. Multimedia Tools and Applications, 80(4), 5173-5186.
[32] Amalia, R., Bustamam, A., Yudantha, A. R., & Victor, A. A. (2021). Diabetic retinopathy detection and captioning based on lesion features using deep learning approach. Commun. Math. Biol. Neurosci., 2021, Article-ID.
[33] Birajdar, U., Gadhave, S., Chikodikar, S., Dadhich, S., & Chiwhane, S. (2020, January). Detection and classification of diabetic retinopathy using AlexNet architecture of convolutional neural networks. In Proceeding of International Conference on Computational Science and Applications: ICCSA 2019 (pp. 245-253). Singapore: Springer Singapore.
[34] Pan, X., Jin, K., Cao, J., Liu, Z., Wu, J., You, K., ... & Ye, J. (2020). Multi-label classification of retinal lesions in diabetic retinopathy for automatic analysis of fundus fluorescein angiography based on deep learning. Graefe’s Archive for Clinical and Experimental Ophthalmology, 258, 779-785.
[35] Tang, L., Niemeijer, M., Reinhardt, J. M., Garvin, M. K., & Abramoff, M. D. (2012). Splat feature classification with application to retinal hemorrhage detection in fundus images. IEEE Transactions on Medical Imaging, 32(2), 364-375.
IECE Transactions on Emerging Topics in Artificial Intelligence
ISSN: 3066-1676 (Online) | ISSN: 3066-1668 (Print)
Email: [email protected]
Portico
All published articles are preserved here permanently:
https://www.portico.org/publishers/iece/