ARCHIVES

1. Cao, W., Shan, J., Czarnek, N., & Li, L. (2017, November). Microaneurysm detection in fundus images using small image patches and
machine learning methods. In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 325-331). IEEE.
2. Shan, J., & Li, L. (2016, June). A deep learning method for microaneurysm detection in fundus images. In 2016 IEEE First International
Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) (pp. 357-358). IEEE.
3. Goel, S., Gupta, S., Panwar, A., Kumar, S., Verma, M., Bourouis, S., & Ullah, M. A. (2021). Deep Learning Approach for Stages of Severity
Classification in Diabetic Retinopathy Using Color Fundus Retinal Images. Mathematical Problems in Engineering, 2021.
4. Qomariah, D. U. N., Tjandrasa, H., & Fatichah, C. (2019, July). Classification of diabetic retinopathy and normal retinal images using
CNN and SVM. In 2019 12th International Conference on Information & Communication Technology and System (ICTS) (pp. 152-157).
IEEE.
5. ATILA, M., LACHGAR, M., HRIMECH, H., & KARTIT, A. (2021). Diabetic Retinopathy Classification Using ResNet50 and VGG-16
Pretrained Networks. International Journal of Computer Engineering and Data Science (IJCEDS), 1(1), 1-7.
6. Yaqoob, M. K., Ali, S. F., Bilal, M., Hanif, M. S., & Al-Saggaf, U. M. (2021). Resnet based deep features and random forest classifier fordiabetic retinopathy detection. Sensors, 21(11), 3883.
7. Carrera, E. V., González, A., & Carrera, R. (2017, August). Automated detection of diabetic retinopathy using SVM. In 2017IEEE XXIV
international conference on electronics, electrical engineering and computing (INTERCON) (pp. 1-4). IEEE.
8. Elswah, D. K., Elnakib, A. A., & Moustafa, H. E. D. (2020, September). Automated diabetic retinopathy grading using resnet. In 2020 37th
National Radio Science Conference (NRSC) (pp. 248-254). IEEE.
9. Bhardwaj, C., Jain, S., & Sood, M. (2021). Diabetic retinopathy severity grading employing quadrant‐based Inception‐V3 convolution
neural network architecture. International Journal of Imaging Systems and Technology, 31(2), 592-608.
10. Bhardwaj, C., Jain, S., & Sood, M. (2021). Deep Learning–Based Diabetic Retinopathy Severity Grading System Employing Quadrant
Ensemble Model. Journal of Digital Imaging, 34(2), 440-457.
11. Elsawah, D., Elnakib, A., & Moustafa, H. E. D. S. (2020). Deep Learning Grading System for Diabetic Retinopathy using Fundus Images.
(Dept. E). MEJ. Mansoura Engineering Journal, 45(4), 1-8.
12. Sopharak, A., Uyyanonvara, B., & Barman, S. (2013). Automated microaneurysm detection algorithms applied to diabetic retinopathy
retinal images. Maejo International Journal of Science and Technology, 7(2), 294.
13. Van Grinsven, M. J., van Ginneken, B., Hoyng, C. B., Theelen, T., & Sánchez, C. I. (2016). Fast convolutional neural network training
using selective data sampling: Application to hemorrhage detection in color fundus images. IEEE transactions on medical imaging.
14. Gurudath, N.; Celenk, M.; Riley, H.B. Machine learning identification of diabetic retinopathy from fundus images. In Proceedings of the
2014 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), Philadelphia, PA, USA, 13 December 2014; pp