A Detailed Study on Diagnosis and Prediction of Diabetic Retinopathy Using Current Machine Learning and Deep Learning Techniques
DOI:
https://doi.org/10.61841/15bwmr94Keywords:
Deep Learning Techniques, Diagnosis and Prediction, Diabetic Retinopathy.Abstract
Diabetic retinopathy is a disease that manifests itself in the retina of the human eye. The effects of the rudimentary stages of this disease include blurred vision, seeing dark spots due to accumulation of blood vessels, and later stages of this disease can cause complete blindness in 90% of cases. The detection and diagnosis of diabetic retinopathy is well established in the field of medicine and can be performed by professionals. The process is known to be expensive and cumbersome. However, the rise of machine learning and AI has paved the path towards disease detection, creating a niche for diabetic retinopathy. This paper reviews the current diabetic retinopathy detection literature and provides an insight into the various computer-aided methods of diabetic retinopathy detection.
Downloads
References
[1] Sohini Roychowdhury, Dara D. Koozekanani, Keshab K. Parhi (2014), “DREAM: Diabetic Retinopathy
Analysis Using Machine Learning”. IEEE Journal of Biomedical and Health Informatics, Vol. 18, No. 5.
[2] Karan Bhatia, Shikhar Arora, and Ravi Tomar. (2016), “Diagnosis of Diabetic Retinopathy Using Machine
Learning Classification Algorithm”, 2nd International Conference on Next Generation Computing
Technologies (NGCT-2016).
[3] Valliappan Raman, P atrick Then, Putra Sumari (2016), “Proposed Retinal Abnormality Detection and
Classification Approach”, 2016 8th IEEE International Conference on Communication Software and
Networks.
[4] Harry Pratt, Frans Coenen, Deborah M Broadbent, Simon P Harding , Yalin Zheng (2016), “ Convolutional
Neural Networks for Diabetic Retinopathy”, International Conference On Medical Imaging Understanding
and Analysis 2016, MIUA 2016.
[5] Saeed Piri, Dursun Delen, Tieming Liu, and Hamed M. Zolbanin. (2017), “ A data analytics approach to
building a clinical decision support system for diabetic retinopathy: Developing and deploying a model
ensemble”, Decision Support Systems 101 (2017).
[6] Javeria Amin, Muhammad Sharif, Mussarat Yasmin, Hussam Ali, Steven Lawrence Fernandes.(2017), “ A
method for the detection and classification of diabetic retinopathy using structural predictors of
lesions”, Journal of Computational Science 19 (2017) 153–164.
[7] Narges Shafaei Bajestani, Ali Vahidian Kamyad, Ensieh Nasli Esfahani, and Assef Zare (2018), “Prediction of
retinopathy in diabetic patients using type-2 fuzzy regression model”, European Journal Of Operational
Research(2018).
[8] Juan Shan, Lin Li. (2016), “ A Deep Learning Method for Microaneurysm Detection in Fundus Images”,
Connected Health: Applications, Systems and Engineering Technologies (CHASE), 2016.
[9] R.F. Mansour (2017), “Deep-learning-based automatic computer-aided diagnosis system for diabetic
retinopathy”, Biomed. Eng. Lett, Springer, 2016.
[10] Darshit Doshi, Aniket Shoney, Deep Sidhpura, Prachi Gharpure (2017), “Diabetic retinopathy detection
using deep convolutional neural networks”, Computing, Analytics and Security Trends (CAST ),
International Conference 2017.
[11] IgiArdiyanto, Hanung Adi Nugroho, Ratna Lestari BudianiBuana (2017), “Deep learning-based diabetes
Retinopathy assessment on embedded system”, Engineering in Medicine and Biology Society (EMBC),
2017.
[12] Gargeya R, Leng T, “Automated Identification of Diabetic Retinopathy Using Deep Learning”, Manuscript
no. 2016-645, AAO Journal (2016).
[13] Leontidis G, “A new unified framework for the early detection of the progression to diabetic retinopathy
from fundus images”, Vol. 90, Computers in Biology and Medicine (2017).
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Author
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
