| Issue |
E3S Web Conf.
Volume 430, 2023
15th International Conference on Materials Processing and Characterization (ICMPC 2023)
|
|
|---|---|---|
| Article Number | 01075 | |
| Number of page(s) | 10 | |
| DOI | https://doi.org/10.1051/e3sconf/202343001075 | |
| Published online | 06 October 2023 | |
Performance Comparison of CNN and DNN Algorithms for Automation of Diabetic Retinopathy Disease
1 Department of CSE, Gokaraju Rangaraju Institute of Engineering and Technology (GRIET), Hyderabad, Telangana State, India
2 UG Student, Department of CSE, GRIET, Hyderabad, Telangana State, India.
3 Uttaranchal Institute of Management, Uttaranchal University, Dehradun, 248007, India.
4 KG Reddy College of Engineering & Technology, Hyderabad.
* Corresponding author: bsankarababu81@gmail.com
Automation of medical image analysis helps medical practitioners to ensure early detection of certain diseases. Diabetic Retinopathy (DR) is a widespread condition of diabetes mellitus and a main global cause of vision impairment. The manual diagnosis of diabetic retinopathy by ophthalmologists requires a significant amount of time, causing inconvenience and discomfort for patients. However, the use of automated technology makes it possible to quickly identify diabetic retinopathy, permitting the continuation of therapy without interruption and averting future ocular damage. This paper presents a comprehensive comparative analysis of six Convolutional Neural Networks and Deep Neural Networks based machine learning models, including simple CNN, VGG16, DenseNet121, ResNet50, InceptionV3, and EfficientNetB3, for the recognition of diabetic retinopathy using fundus photographs. The accuracy of various models is evaluated using the Cohen Kappa metric. The results of this study add a contribution to the research on the application of machine learning models for diagnosing diabetic retinopathy.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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