Authors : Sujapriya S, John Raj I

Volume/Issue : Volume 9 - 2024, Issue 6 - June

Google Scholar : https://tinyurl.com/3jantdun

Scribd : https://tinyurl.com/yntyj7mb

DOI : https://doi.org/10.38124/ijisrt/IJISRT24JUN1899

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The proposed framework merges Generative Adversarial Networks (GANs) with Reinforcement Learning (RL) techniques to enhance blindness detection. GANs generate synthetic retinal images covering various eye diseases, enriching training data and improving generalization. RL optimizes screening strategies dynamically, adjusting decisions based on evolving patient profiles and environmental cues. Empirical evaluations on real-world datasets demonstrate superior performance over conventional methods, addressing data imbalance and fostering adaptable screening policies. This synergistic fusion offers a comprehensive, adaptable, and interpretable approach to early diagnosis and preventive care, highlighting the potential of advanced AI techniques in healthcare.

Keywords : Generative Adversarial Networks, Reinforcement Learning, Healthcare and Patient.

References :

1. Bilal, A.; Zhu, L.; Deng, A.; Lu, H.; Wu, N. AI-Based Automatic Detection and Classification of Diabetic  RetinopathyUsing U-Net 293 and Deep Learning. SYMMETRY-BASEL 2022,14, 1955–1977.
2. Shaban, M.; Mahmoud, A.H.; Shalaby, A.; Ghazal, M.; Sandhu, H.; El-Baz, A. Low - complexity computer-aided     diagnosis for diabetic retinopathy. In Diabetes and Retinopathy; Elsevier: Amsterdam, The Netherlands, 2020.
3. Kanimozhi, J.; Vasuki, P.; Roomi, S.M.M. Fundus image lesion detection algorithm for diabetic retinopathy screening. J. Ambient.Intell. Humaniz. Comput. 2020,12, 7407-7416.
4. Manjaramkar, A.; Kokare, M. Automated Red Lesion Detection: An Overview. In Advances in Intelligent Systems and Computing;Springer: Singapore, 2020; Volume 1089.
5. Bilal, A.; Sun, G.; Mazhar, S.; Imran, A.; Latif, J. A Transfer Learning and U-Net- based automatic detection of diabetic retinopathy from fundus images. Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 202.
6. F. Bandello, M. A. Zarbin, R. Lattanzio and I. Zucchiatti, Clinical Strategies in the Management of Diabetic Retinopathy, Berlin Heidelberg:Springer-Verlag, 2014.
7. B. Lumbroso, M. Rispoli and M. C. Savastano, Diabetic Retinopathy, Jaypee Brothers Medical Publisher, 2015.
8. A. Akhter, K. Fatema, S. F. Ahmed, A. Afroz, L. Ali and A. Hussain, "Prevalence and associated risk indicators of retinopathy in a rural Bangladeshi population with and without diabetes", Ophthalmic Epidemiol., vol. 20, no. 4, pp. 220-7, Aug. 2013.
9. M. S. Chowdhury, F. R. Taimy, N. Sikder and A.-A. Nahid, "Diabetic Retinopathy Classification with a Light Convolutional Neural Network", 2019 International Conference on Computer Communication Chemical Materials and Electronic Engineering (IC4ME2).
10. M. U. Akram, S. Khalid and S. A. Khan, "Identification and classification of microaneurysms for early detection of diabetic retinopathy", Pattern Recognit., vol. 46, no. 1, pp. 107-116, Jan. 2013.
11. M. U. Akram, S. Khalid, and S. A. Khan,“Identification and classification of microaneurysms for early detection of diabetic retinopathy,” Pattern Recognit., vol. 46, no. 1, pp. 107–116, Jan. 2013.
12. E. Saleh et al., “Learning ensemble classifiers for diabetic retinopathy assessment,” Artif. Intell. Med.,vol. 85, pp. 50–63, Apr. 2018.
13. N. Sikder, M. S. Chowdhury, A. M. Shamim Arif, and A.-A. Nahid, “Human Activity Recognition Using Multichannel Convolutional Neural Network,”2019 5th Int. Conf. Adv. Electr. Eng., 2019.
14. B. Lumbroso, M. (Ophthalmologist) Rispoli, and M.C. Savastano, Diabetic retinopathy. .
15. Lakshminarayanan,  V.,  Kheradfallah,  H.,  Sarkar,  A.,  &  Jothi  Balaji,  J.  (2021).  Automated  Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey. Journal of Imaging, 7(9), 165.
16. Hasan, D. A., Zeebaree, S. R., Sadeeq, M. A., Shukur, H. M., Zebari, R. R., & Alkhayyat, A. H. (2021, April).  Machine  Learning-based  Diabetic  Retinopathy  Early  Detection  and  Classification Systems-A Survey.  In 2021  1st  Babylon  International  Conference  on  Information  Technology  and  Science (BICITS) (pp. 16-21). IEEE.
17. Moshfeghi, D. M., & Trese, M. T. (2021). Reducing blindness resulting from retinopathy of prematurity using deep learning. Ophthalmology, 128(7), 1077-1078.
18. Tymchenko,  B.,  Marchenko,  P.,  &  Spodarets,  D.  (2020).  Deep  learning  approach  to  diabetic retinopathy detection. arXiv preprint arXiv:2003.02261.
19. A. Iqbal, et al. Generative adversarial networks and its applications in the biomedical image segmentation: a comprehensive survey International Journal of Multimedia Information Retrieval (2022), pp. 1-36