Deep Dive Into Diabetic Retinopathy Identification: A Deep Learning Approach with Blood Vessel Segmentation and Lesion Detection

Authors

  • Kamal Upreti CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India
  • Anmol Kapoor Maharaja Surajmal Institute of Technology, Delhi, India
  • Sheela Hundekari MIT ADT University, Pune, India
  • Shitiz Upreti Maharishi Markandeshwar (Deemed to be University) Mullana-Ambala, Haryana, India
  • Kajal Kaul Bharati Vidyapeeth College of Engineering, Rohini, India
  • Shreya Kapoor Dr. Akhilesh Das Gupta Institute of Technology & Management, New Delhi, India
  • Akhilesh Tiwari CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India

DOI:

https://doi.org/10.13052/jmm1550-4646.20210

Keywords:

Deep learning, diabetic retinopathy, lesions, soft exudates, hemorrhages, microaneurysms

Abstract

In the landscape of diabetes-related ocular complications, diabetic retinopathy stands as a formidable challenge, reigning as the leading cause of vision impairment worldwide. Despite extensive research, the quest for effective treatments remains an ongoing pursuit. This study explores the burgeoning domain of AI-driven approaches in ocular research, particularly focusing on diabetic retinopathy detection. It delves into various diagnostic methodologies, encompassing the detection of microaneurysms, identification of hemorrhages, and segmentation of blood vessels, primarily utilizing retinal fundus photographs. Our findings juxtapose conventional machine learning techniques against deep neural networks, showcasing the remarkable efficacy of Convolutional neural network (CNN) and Random Forest (RF) in segmenting blood vessels and the robustness of deep learning in lesion identification. As we navigate the quest for clearer vision, artificial intelligence takes center stage, promising a transformative leap forward in the realm of vision care.

Downloads

Download data is not yet available.

Author Biographies

Kamal Upreti, CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India

Kamal Upreti is currently working as an Associate Professor in Department of Computer Science, CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India. He completed is B. Tech (Hons) Degree from UPTU, M. Tech (Gold Medalist), PGDM(Executive) from IMT Ghaziabad and PhD in Department of Computer Science & Engineering. Recently, he has completed Postdoc from National Taipei University of Business, TAIWAN funded by MHRD.

He has published 50+ Patents, 45+ Books, 32+Magazine issues and 100+ Research papers in in various international Conferences and reputed Journals. His areas of Interest are Data Analytics, Cyber Security, Machine Learning, Health Care, Embedded System and Cloud Computing. He is having enriched years’ experience in corporate and teaching experience in Engineering Colleges.

He has attended as a Session Chair Person in National, International conference and key note speaker in various platforms such as Skill based training, Corporate Trainer, Guest faculty and faculty development Programme. He awarded as best teacher, best researcher, extra academic performer and Gold Medalist in M. Tech programme.

Anmol Kapoor, Maharaja Surajmal Institute of Technology, Delhi, India

Anmol Kapoor has completed his graduation in Department of Computer Science and Engineering from Guru Gobind Singh Indraprastha University. He has embraced a career as a software engineer. He is having more enthusiasm in exploring the forefront of technology, particularly Machine Learning (ML) and Blockchain. Through dedicated research, he has investigated ML’s role in revolutionizing healthcare and Blockchain’s potential for transformative impact. Currently, his focus areas include diabetic retinopathy analysis, object tracking, and sentiment analysis in finance, where he strive to bridge the gap between technology and real-world applications. Beyond my professional pursuits, he is actively engage in hackathons, mentorship programs, and community initiatives, seeking to contribute to the tech ecosystem.

Sheela Hundekari, MIT ADT University, Pune, India

Sheela Hundekari, presently working as Associate Professor in MITCOM, MCA dept. at MIT ADT University, Loni kalbhor, Pune 412201, Her research interests are Artificial intelligence, Machine Learning, Deep Learning. She has published research papers in reputed Scopus and Springer Journals. A certified full stack Java trainer.

Shitiz Upreti, Maharishi Markandeshwar (Deemed to be University) Mullana-Ambala, Haryana, India

Shitiz Upreti is currently working as an Assistant Professor (MBA) in the Department of IT & Management, Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, Haryana. He has around 11 years of experience in the field of teaching, training and research development. He completed his B.Tech and M.Tech in the field of Electronics & Communication Engineering. He also done MBA in the field of IT & Operation Research. Currently, pursuing Ph.D. (Final Viva Pending) in the field of IT & Wireless Communication Engineering.

His area of interest includes Wireless communication, Machine Learning, Cloud Computing and Data Analytics. He also attended various FDPs and workshops regarding Machine Learning, SPSS & Blockchain Technology.

Kajal Kaul, Bharati Vidyapeeth College of Engineering, Rohini, India

Kajal Kaul is working as an Assistant Professor at Bharati Vidyapeeth College of Engineering, New Delhi. She has a teaching experience of 3 years in this field. She is pursuing her PhD in Computer Science Engineering from University School of Information, Communication & Technology(USICT), Guru Gobind Singh Indraprastha University(GGSIPU). Her research interest is Deep Learning, Image Processing, IOT, etc.

Shreya Kapoor, Dr. Akhilesh Das Gupta Institute of Technology & Management, New Delhi, India

Shreya Kapoor is currently doing B.Tech in department of Computer Science and Engineering at Guru Gobind Singh Indraprastha University. Her academic journey aligns with a deep interest in cutting-edge technologies, specifically ML in healthcare and Blockchain. Through dedicated research, she has explored these fields, leading to a significant research paper. This work delves into the potential of ML to revolutionize healthcare while leveraging the transformative impact of blockchain technology. She has more enthusiastic about contributing fresh insights at the intersection of these domains, driven by a genuine passion for advancing technology’s role in healthcare. In upcoming research, She is exploring diabetic retinopathy analysis, object tracking, and sentiment analysis in finance.

Akhilesh Tiwari, CHRIST (Deemed to be University), Delhi NCR, Ghaziabad, India

Akhilesh Tiwari, an MBA and PhD holder, boasts 23 years of teaching experience. With a consistent academic performance, he excels in teaching and researching subjects such as Quantitative Techniques, Operations Research, Operations Management, Digital Marketing, Business Mathematics and Statistics, E-Business, E-Commerce, and Supply Chain Management. As a consultant and external trainer, Dr. Tiwari has conducted successful workshops on various Operation Research Models and Optimization Techniques for esteemed institutions including the Armed Forces, University of Allahabad, and Ericsson India Ltd.

Over his 23-year academic journey, he has groomed management students in subjects like Operation Research, Quantitative Techniques, Digital Marketing, E-Business, E-commerce and Production and Operations Management. Currently pursuing post-doctoral challenges (D. Litt) to explore the impact of online marketing on consumer behavior, Dr. Tiwari has also led the introduction of new management programs at Devi Ahilya University Indore, including Hospital Administration, E-Commerce, and Financial Administration. He is the author of three textbooks published by Shail Publications Allahabad, namely “Fundamentals of Operations Research,” “Business Statistics and Mathematics,” and a PhD entrance test book. His extensive experience encompasses a range of academic, administrative, and developmental assignments crucial to management institutes, including organizing management conferences, conducting training programs, facilitating faculty development, managing examinations, coordinating specialization programs, and overseeing student cultural affairs and summer training projects

References

Bidwai P, Gite S, Pahuja K, Kotecha K. A Systematic Literature Review on Diabetic Retinopathy Using an Artificial Intelligence Approach. Big Data and Cognitive Computing. 2022; 6(4):152. https://doi.org/10.3390/bdcc6040152.

Wejdan L. Alyoubi, Wafaa M. Shalash, Maysoon F. Abulkhair, Diabetic retinopathy detection through deep learning techniques: A review, Informatics in Medicine Unlocked, Volume 20, 2020, 100377, ISSN 2352-9148, https://doi.org/10.1016/j.imu.2020.100377.

Kusuhara, Sentaro, Fukushima, Yoko, Ogura, Shuntaro, Inoue, Naomi, Uemura, Akiyoshi. (2018). Pathophysiology of Diabetic Retinopathy: The Old and the New. Diabetes & Metabolism Journal. 42. 364. doi: 10.4093/dmj.2018.0182.

S. Qummar et al., “A Deep Learning Ensemble Approach for Diabetic Retinopathy Detection,” in IEEE Access, vol. 7, pp. 150530–150539, 2019, doi: 10.1109/ACCESS.2019.2947484.

Nakayama Luis Filipe, Ribeiro Lucas Zago, Malerbi Fernando Korn, Regatieri Caio Vinicius Saito, Ophthalmology and Artificial Intelligence: Present or Future? A Diabetic Retinopathy Screening Perspective of the Pursuit for Fairness, Frontiers in Ophthalmology, 2, 2022. 2674-0826, doi: 10.3389/fopht.2022.89818. https://www.frontiersin.org/articles/10.3389/fopht.2022.898181.

Varma, R., Bressler, N. M., Doan, Q. V., Danese, M., Dolan, C. M., Lee, A., and Turpcu, A. (2015). Visual impairment and blindness avoided with ranibizumab in Hispanic and non-Hispanic whites with diabetic macular edema in the United States. Ophthalmology, 122(5), 982–989.

Mehta, H., Tufail, A., Daien, V., Lee, A. Y., Nguyen, V., Ozturk, M., …and Gillies, M. C. (2018). Real-world outcomes in patients with neovascular age-related macular degeneration treated with intravitreal vascular endothelial growth factor inhibitors. Progress in retinal and eye research, 65, 127–146.

Abràmoff, M. D., Folk, J. C., Han, D. P., Walker, J. D., Williams, D. F., Russell, S. R., …and Niemeijer, M. (2013). Automated analysis of retinal images for detection of referable diabetic retinopathy. JAMA ophthalmology, 131(3), 351–357.

Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., …and Webster, D. R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. jama, 316(22), 2402–2410.

Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C., Liang, H., Baxter, S. L., …and Zhang, K. (2018). Identifying medical diagnoses and treatable diseases by image-based deep learning. cell, 172(5), 1122–1131.

W. M. Gondal, J. M. Köhler, R. Grzeszick, G. A. Fink and M. Hirsch, “Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images”, Proc. IEEE Int. Conf. Image Process. (ICIP), pp. 2069–2073, Sep. 2017.

Z. Wang, Y. Yin, J. Shi, W. Fang, H. Li and X. Wang, “Zoom-in-net: Deep mining lesions for diabetic retinopathy detection” in Int. Conf. Med. Image Comput. Comput.-Assist. Intervent, Berlin, Germany:Springer, pp. 267–275, 2017.

T. Chandrakumar and R. Kathirvel, “Classifying diabetic retinopathy using deep learning architecture”, Int. J. Eng. Res. Technol., vol. 5, no. 6, pp. 19–24, Jun. 2016.

H. Pratt, F. Coenen, D. M. Broadbent, S. P. Harding and Y. Zheng, “Convolutional neural networks for diabetic retinopathy”, Procedia Comput. Sci., vol. 90, pp. 200–205, Jan. 2016.

Y. Yang, T. Li, W. Li, H. Wu, W. Fan and W. Zhang, “Lesion detection and grading of diabetic retinopathy via two-stages deep convolutional neural networks”, Proc. Int. Conf. Med. Image Comput. Comput.-Assist. Intervent, pp. 533–540, 2017.

Shu-I Pao, Hong-Zin Lin, Ke-Hung Chien, Ming-Cheng Tai, Jiann-Torng Chen, Gen-Min Lin, “Detection of Diabetic Retinopathy Using Bichannel Convolutional Neural Network”, Journal of Ophthalmology, vol. 2020, Article ID 9139713, 7 pages, 2020. https://doi.org/10.1155/2020/9139713.

Gao Jinfeng, Sehrish Qummar, Zhang Junming, Yao Ruxian, Fiaz Gul Khan, “Ensemble Framework of Deep CNNs for Diabetic Retinopathy Detection”, Computational Intelligence and Neuroscience, vol. 2020, Article ID 8864698, 11 pages, 2020. https://doi.org/10.1155/2020/8864698.

Muhammad Mateen, Junhao Wen, Nasrullah Nasrullah, Song Sun, Shaukat Hayat, “Exudate Detection for Diabetic Retinopathy Using Pretrained Convolutional Neural Networks”, Complexity, vol. 2020, Article ID 5801870, 11 pages, 2020. https://doi.org/10.1155/2020/5801870.

D. Siva Sundhara Raja, S. Vasuki, “Automatic Detection of Blood Vessels in Retinal Images for Diabetic Retinopathy Diagnosis”, Computational and Mathematical Methods in Medicine, vol. 2015, Article ID 419279, 12 pages, 2015. https://doi.org/10.1155/2015/419279.

Nagaraja Gundluru, Dharmendra Singh Rajput, Kuruva Lakshmanna, Rajesh Kaluri, Mohammad Shorfuzzaman, Mueen Uddin, Mohammad Arifin Rahman Khan, “Enhancement of Detection of Diabetic Retinopathy Using Harris Hawks Optimization with Deep Learning Model”, Computational Intelligence and Neuroscience, vol. 2022, Article ID 8512469, 13 pages, 2022. https://doi.org/10.1155/2022/8512469.

Gupta, Ankita and Chhikara, Rita. (2018). Diabetic Retinopathy: Present and Past. Procedia Computer Science. 132. 1432–1440. doi: 10.1016/j.procs.2018.05.074.

Wang, Shuangling, et al. (2015) “Hierarchical retinal blood vessel segmentation based on feature and ensemble learning.” Neurocomputing 149: 708–717.

Liskowski, Paweł, and Krzysztof Krawiec.(2016) “Segmenting retinal blood vessels with deep neural networks.” IEEE transactions on medical imaging 35(11): 2369–2380.

Lupascu, Carmen Alina, Domenico Tegolo, and Emanuele Trucco. (2010) “FABC: retinal vessel segmentation using AdaBoost.” IEEE Transactions on Information Technology in Biomedicine 14(5): 1267–1274.

Odstrcilik, Jan, et al. (2013) “Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database.” IET Image Processing 7(4): 373–383.

Fraz, Muhammad Moazam, et al. (2012) “An ensemble classification-based approach applied to retinal blood vessel segmentation.” IEEE Transactions on Biomedical Engineering 59(9): 2538–2548.

Antal, Bálint, and András Hajdu. (2014) “An ensemble-based system for automatic screening of diabetic retinopathy.” Knowledge-based systems 60: 20–27.

Abràmoff, Michael David, et al. (2016) “Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning.” Investigative ophthalmology & visual science 57(13): 5200–5206.

Singh, Anju, Singh, Divakar, Sharma, Shikha, Upreti, Kamal, Maheshwari, Manish, Mehta, Vimal, Sharma, Jitender, Mehra, Pratishtha and Dabla, Pradeep. (2022). Discovering Patterns of Cardiovascular Disease and Diabetes in Myocardial Infarction Patients Using Association Rule Mining. Folia Medica Indonesiana. 58. 242–250. doi: 10.20473/fmi.v58i3.34975.

Roychowdhury, Sohini, Dara D. Koozekanani, and Keshab K. Parhi. (2014) “Dream: Diabetic retinopathy analysis using machine learning.” IEEE journal of biomedical and health informatics 18(5): 1717–1728.

Kapoor, Anmol, Kapoor, Shreya, Upreti, Kamal, Singh, Prashant, Kapoor, Seema, Alam, Mohammad, and Nasir, Mohammad. (2023). Cardiovascular Disease Prognosis and Analysis Using Machine Learning Techniques. doi: 10.1007/978-3-031-25088-0_15.

Singh, Anju, Singh, Divakar, Sharma, Shikha, Upreti, Kamal, Maheshwari, Manish, Mehta, Vimal, Sharma, Jitender, Mehra, Pratishtha, Dabla, Kumar and Dabla, Pradeep. (2022). Original Research Report Discovering Patterns of Cardiovascular Disease and Diabetes in Myocardial Infarction Patients using Association Rule Mining. 242–250. doi: 10.20473/fmi.v58i3.34975.

Rakhlin, Alexander. (2018). Diabetic Retinopathy detection through integration of Deep Learning classification framework. doi: 10.1101/ 225508.

S. Qummar et al., “A Deep Learning Ensemble Approach for Diabetic Retinopathy Detection,” in IEEE Access, vol. 7, pp. 150530–150539, 2019, doi: 10.1109/ACCESS.2019.2947484.

Downloads

Published

2024-03-29

How to Cite

Upreti, K., Kapoor, A., Hundekari, S., Upreti, S., Kaul, K., Kapoor, S., & Tiwari, A. (2024). Deep Dive Into Diabetic Retinopathy Identification: A Deep Learning Approach with Blood Vessel Segmentation and Lesion Detection. Journal of Mobile Multimedia, 20(02), 495–524. https://doi.org/10.13052/jmm1550-4646.20210

Issue

2024: Vol 20 Iss 2

Section

Articles

Most read articles by the same author(s)