ABSTRACT:
Diabetic Retinopathy is a major threat to cause vision loss in people suffering from Diabetes Mellitus. Many machine learning algorithms were proposed to detect Diabetic Retinopathy (DR) at an early stage, and with proper treatment vision loss may be reduced. This paper proposes a novel method to detect DR through severity scale by observing the abnormalities through ensemble methods. Deep learning based models are gaining focus to construct automated tools for medical image analysis. This paper uses Alex Net based DNN (Deep Neural Network) which functions on the basis of Convolution Neural Network (CNN) and is applied to have an optimal solution for automated DR detection with Random Forest Classifier (RFC). Recursively Separated and Weighted Histogram Equalisation (RSHWE) is used to preserve brightness, ensemble of segmentation algorithms to the identify Region of Interest (ROI). Feature map constructed using Gaussian and Gabor filter coefficients and Grey Level Co occurrence Matrix (GLCM) features and these features are applied to Random Forest Classifier (RFC) to classify the diseased images. The performance of RFC is also compared with and without Gradient features with Enhanced RFC (E-RFC). The accuracy of various classifiers is compared with our proposed method. In this paper, the considered performance metrics are accuracy, sensitivity, specificity. This method experimented on publicly available fundus image data sets for DR and shows good results with an accuracy (94.8%), specificity (93%), sensitivity (96%).
Cite this article:
Lavanya Ravala, Rajini G.K.. Optimized Deep Learning based Approach for Enhanced frame work of Automated Diagnosis of Diabetic Retinopathy. Research Journal of Pharmacy and Technology. 2024; 17(9):4443-8. doi: 10.52711/0974-360X.2024.00686
Cite(Electronic):
Lavanya Ravala, Rajini G.K.. Optimized Deep Learning based Approach for Enhanced frame work of Automated Diagnosis of Diabetic Retinopathy. Research Journal of Pharmacy and Technology. 2024; 17(9):4443-8. doi: 10.52711/0974-360X.2024.00686 Available on: https://rjptonline.org/AbstractView.aspx?PID=2024-17-9-47
REFERENCES:
1. Shaik Naseera, G.K. Rajini, B. Venkateswarlu, Jasmin Pemeena Priyadarisini M. A Review on Image Processing Applications in Medical Field. Research J. Pharm. and Tech. 2017; 10(10): 3556-3560, DOI:10.5958/0974-360X.2017.00644.8
2. S. K. Mittal, P. Nishant, A. Agrawal, S. Kumari, P. Kumar, and A. Chawhan. Community screening for diabetic retinopathy in uttarakhand, india, through targeted camps–a retrospective survey. Indian Journal of Community Ophthalmology. 2020; 1: 19–21.
3. Anannya Bose, Susanta Paul, Dibya Das, Tathagata Roy, Vinay Kumar Pandey. Recent Advancement of Nanomedicine for Diabetic Retinopathy: A Review. Research Journal of Pharmacy and Technology. 2023; 16(7): 3507-0.
4. Ankita Gupta, Rita Chhikara. Diabetic Retinopathy: Present and Past. Procedia Computer Science. 2018; 132: 1432-1440 https://doi.org/10.1016/j.procs.2018.05.074
5. Nandhini Murali, Abinaya S.K, Saveetha. V. Knowledge, attitude and practice of Diabetic Retinopathy among type II diabetic patients of South Indian population. Research J. Pharm. and Tech. 2017; 10(9): 3017-3021
6. A. H. Vyas and V. Khanduja. A Survey on Automated Eye Disease Detection using Computer Vision Based Techniques. 2021 IEEE Pune Section International Conference (PuneCon). 2021; 1-6. doi: 10.1109/PuneCon52575.2021.96864
79.
7. P. Vijay Daniel, D. Pamela, P. Kingston Stanley, J. Samson Issac. Digital Diagnosis of Diabetic Retinopathy using Fundus Images. Research J. Pharm. and Tech. 2019; 12(2): 717-722.
8. Reza AM. Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement. Journal of VLSI Signal Processing Systems for Signal, Image and Video Technology. 2004; 38(1): 35–44
9. García-Lamont F, Cervantes J, López-Chau A, Ruiz S. editors. Contrast Enhancement of RGB Color Images by Histogram Equalization of Color Vectors’ Intensities. International Conference on Intelligent Computing. Springer, 2018
10. M. Kim and M. G. Chung. Recursively separated and weighted histogram equalization for brightness preservation and contrast enhancement. IEEE Transactions on Consumer Electronics. 2008; 54(3): 1389-1397. doi: 10.1109/TCE.2008.4637632.
11. Kuldeep Singh, Rajiv Kapoor, Sanjeev Kr. Sinha. Enhancement of low exposure images via recursive histogram equalization algorithms. Optik. 2015; 126(20): 2619-2625.
12. Ganesan P, B.S. Sathish, L.M.I. Leo Joseph, K.M. Subramanian, V. Kalist5, K. Vasanth. Retinal Blood Vessels and Optical Disc Segmentation in Branch Retinal Vein Occluded Fundus Images Using Digital Image Processing Techniques. Research J. Pharm. and Tech. 2019; 12(4): 1901-1906.
13. R. G. Bozomitu, A. Păsărică, V. Cehan, R. G. Lupu, C. Rotariu and E. Coca, Implementation of eye-tracking system based on circular Hough transform algorithm, 2015 E-Health and Bioengineering Conference (EHB), 2015; 1-4. doi: 10.1109/EHB.2015.7391384.
14. Christopher Jose, D. Aju. A Hybrid Method for Diabetic Retinopathy Diagnosis through Blood Vessel Extraction and Exudates Identification from 2D Fundus Image’. Research J. Pharm. and Tech. 2018; 11(3): 1147-1152.
15. H. Wan, H. Wang, B. Scotney and J. Liu. A Novel Gaussian Mixture Model for Classification. 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2019; 3298-3303, doi: 10.1109/SMC.2019.8914215
16. A. Sopharak, B. Uyyanonvara, S. Barman, T.H. Williamson. Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods. Comput. Med. Imag. Graph. 2008; 32(8): 720-727.
17. J. Kaur, D. Mittal. A generalized method for the segmentation of exudates from pathological retinal fundus images. Biocybernetics and Biomedical Engineering. 2018; 38(1): 27-53.
18. M. Iyapparaja, P. Sivakumar. Detecting Diabetic Retinopathy exudates in digital image processing Hybrid Methodology. Research J. Pharm. and Tech. 2019; 12(1): 57-61.
19. A. D. Fleming, S. Philip, K. A. Goatman, J. A. Olson and P. F. Sharp. Automated microaneurysm detection using local contrast normalization and local vessel detection. IEEE Transactions on Medical Imaging. 2006; 25(9): 1223-1232.
20. Bhimavarapu U, Battineni G. Automatic Microaneurysms Detection for Early Diagnosis of Diabetic Retinopathy Using Improved Discrete Particle Swarm Optimization. J Pers Med. 2022 12(2):317. doi: 10.3390/jpm12020317. PMID: 35207805; PMCID: PMC8878235.
21. Veena Mayya, Sowmya Kamath S․, Uma Kulkarni. Automated microneurysms detection for early diagnosis of diabetic retinopathy: A Comprehensive review.Computer Methods and Programs in Biomedicine Update. 2021; 1: 2666-9900, doi.org/10.1016/j.cmpbup.2021.100013.
22. B.D. Venkatramana Reddy, T. Jayachandra Prasad. Color-Texture Image Segmentation Algorithms based on Hypercomplex Gabor Analysis. Research J. Engineering and Tech. 2011; 2(2): 77-86.
23. Haralick, R. M., Shanmugam, K. and Dinstein, I. Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics. 1973; 3: 610–621, doi:10.1109/TSMC.1973.4309314
24. R. Roslan and N. Jamil. Texture feature extraction using 2-D Gabor Filters. 2012 International Symposium on Computer Applications and Industrial Electronics (ISCAIE), 2012; 173-178. doi: 10.1109/ISCAIE.2012.6482091.
25. Shaukat, N.; Amin, J.; Sharif, M.I.; Sharif, M.I.; Kadry, S.; Sevcik, L. Classification and Segmentation of Diabetic Retinopathy: A Systemic Review. Appl. Sci. 2023; 13: 3108. https://doi.org/10.3390/app13053108
26. Mujeeb Rahman, K.K.; Nasor, M.; Imran, A. Automatic Screening of Diabetic Retinopathy Using Fundus Images and Machine Learning Algorithms. Diagnostics. 2022; 12: 2262.
https://doi.org/10.3390/diagnostics12092262
27. F. Fernández-Navarro, M. Carbonero-Ruz, D. Becerra Alonso and M. Torres-Jiménez. Global Sensitivity Estimates for Neural Network Classifiers. IEEE Transactions on Neural Networks and Learning Systems. 2017; 28(11): 2592-2604.
28. Uddin, S., Khan, A., Hossain, M. E. and Moni, M. A. Comparing different supervised machine learning algorithms for disease prediction. BMC Med. Inform. Decis. Mak. 2019;1: 1–16
29. G-K, R. and Lavanya, R. Diabetic Retinopathy Image Classification Using Transfer Learning. Lecture Notes in Electrical Engineering. 2021; 700: 2511–2524. doi: 10.1007/978-981-15-8221-9_234
30. Khalifa NEM, Loey M, Taha MHN, Mohamed HNET. Deep Transfer Learning Models for Medical Diabetic Retinopathy Detection. Acta Inform Med. 2019; 27(5): 327-332. doi: 10.5455/aim.2019.27.327-332. PMID: 32210500; PMCID: PMC7085308.
31. F. Fernández-Navarro, M. Carbonero-Ruz, D. Becerra Alonso and M. Torres-Jiménez. Global Sensitivity Estimates for Neural Network Classifiers. IEEE Transactions on Neural Networks and Learning Systems. 2017; 28(11): 2592-2604.
32. Yaqoob, M.K.; Ali, S.F.; Bilal, M.; Hanif, M.S.; Al-Saggaf, U.M. ResNet Based Deep Features and Random Forest Classifier for Diabetic Retinopathy Detection. Sensors. 2021; 21: 3883.
33. Yaqoob, M. Kashif, Syed Farooq Ali, Irfan Kareem, and Muhammad MoazamFraz. Feature-based optimized deep residual network architecture for diabetic retinopathy detection. In 2020 IEEE 23rd International Multitopic Conference (INMIC)-2020; 1–6. IEEE, 2020
34. Himansu Das, Bighnaraj Naik, H.S. Behera, A Jaya. Algorithm based wrapper method for optimal feature selection in supervised classification. Journal of King Saud University - Computer and Information Sciences. 2022; 34(6): 3851-3863.