Automated Diagnosis of Diabetic Retinopathy using Deep Learning and Image Analysis

Authors

  • Sujeeth Babu Kolli Department of Computer Science & Engineering,Koneru Lakshmaiah Education Foundation, Green Fields,Vaddeswaram, A.P– 522302
  • Vinay Kalisetti Department of Computer Science & Engineering,Koneru Lakshmaiah Education Foundation, Green Fields,Vaddeswaram, A.P– 522302
  • Rakesh Varaparla Department of Computer Science & Engineering,Koneru Lakshmaiah Education Foundation, Green Fields,Vaddeswaram, A.P– 522302
  • Vamsi Sai Chandra Vasarla Department of Computer Science & Engineering,Koneru Lakshmaiah Education Foundation, Green Fields,Vaddeswaram, A.P– 522302
  • Veeraswamy Ammisetty Associate Professor, Department of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, A.P– 522302

Keywords:

Classification, Convolutional Neural Network, Deep Learning, Diabetic Retinopathy, Fundus Images, Image Analysis, Medical Image Classification

Abstract

Diabetic Retinopathy (DR) is a significant cause of blindness and one of the principal complications of diabetes. For proper management and therapy, early identification and precise assessment of DR intensity levels are critical. In this paper, we present an automated identification approach based on deep learning and visual analysis technology. We categorize fundus photos using a convolutional neural network (CNN) based on the strength of their DR. Our strategy involves altering the photographs, constructing a good CNN design, and utilizing a massive dataset to train the model. The findings indicate how effectively our technology works to detect actual drug leftovers.

Downloads

Download data is not yet available.

References

McKinney, W. (2010). Data Structures for Statistical Computing in Python. In Proceedings of the 9th Python in Science Conference, 51-56.

Hunter, J. D. (2007). Matplotlib: A 2D Graphics Environment. Computing in Science & Engineering, 9(3), 90-95.

Clark, A., DeLong, A., & Poynton, C. (2015). Pillow (PIL Fork) Documentation. Retrieved from https://pillow.readthedocs.io/en/stable/index.html

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Vanderplas, J. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825-2830.

Chollet, F. et al. (2015). Keras. Retrieved from https://keras.io

Abadi, M. et al. (2015). TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Retrieved from https://www.tensorflow.org/

van der Walt, S., Colbert, S. C., & Varoquaux, G. (2011). The NumPy Array: A Structure for Efficient Numerical Computation. Computing in Science & Engineering, 13(2), 22-30.

Waskom, M. (2020). seaborn: statistical data visualization. Journal of Open Source Software, 5(53), 2158.

Google Research. (2018). TensorBoard: TensorFlow's Visualization Toolkit. Retrieved from https://www.tensorflow.org/tensorboard

Hinton, G., Vinyals, O., & Dean, J. (2012). Neural Network for Machine Learning. Coursera. Retrieved from https://www.coursera.org/lecture/deep-neural-networks/neural-networks-with-early-stopping-WlPv9x

Downloads

Published

24.03.2024

How to Cite

Kolli, S. B. ., Kalisetti, V. ., Varaparla, R. ., Chandra Vasarla, V. S. ., & Ammisetty, V. . (2024). Automated Diagnosis of Diabetic Retinopathy using Deep Learning and Image Analysis. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 174–179. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5238

Issue

Vol. 12 No. 3 (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.