Enhanced Recognition system for Diabetic Retinopathy using Machine Learning with Deep Learning Approach
Main Article Content
Keywords
Diabetic Retinopathy, Deep Learning, Machine Learning, Convolutional Neural Network, Support Vector Machine
Abstract
Diabetic is the primary main reasons for Diabetic Retinopathy (DR). If Diabetic Retinopathy is untreated for long term, then it leads to total eye blindness. Now days, prevention of DR is a major challenging task, and moreover it reduces the overall risk of eye blindness. Machine learning and Deep learning is valuable methods to identifying and aiding DR diagnosis. In this paper, new method is proposed by Machine learning and Deep learning technique. In this proposed system, Kaggle dataset is used for training and testing. Totally 3662 images, in which the 2744 images is used to train and remaining 546 images are used to test the model. This system involves classifying using Convolution Neural Network (CNN), Support Vector Machines (SVM). The simulated results are obtained for the classifiers and its outputs are shown in the paper. From the result, it is found that, the CNN Classifier performs well in term of accuracy to detect diabetic retinopathy, compared with the SVM classifier.
References
1. R. S. Rajkumar, T. Jagathishkumar, D. Ragul and A. G. Selvarani, "Transfer Learning Approach for Diabetic Retinopathy Detection using Residual Network," 6th International Conference on Inventive Computation Technologies (ICICT), pp. 1189-1193, 2021, doi: 10.1109/ICICT50816.2021.9358468.
2. P. W. Sudarmadji, P. DevianiPakan, R. YefrenesDillak, "Diabetic Retinopathy Stages Classification using Improved Deep Learning," International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS), pp. 104-109, 2020, doi: 10.1109/ICIMCIS51567.2020.9354281.
3. A. Lands, A. J. Kottarathil, A. Biju, E. M. Jacob and S. Thomas, "Implementation of deep learning based algorithms for diabetic retinopathy classification from fundus images," International Conference on Trends in Electronics and Informatics (ICOEI), pp. 1028-1032, 2020, doi: 10.1109/ICOEI48184.2020.9142878.
4. R. N. Lazuardi, N. Abiwinanda, T. H. Suryawan, M. Hanif and A. Handayani, "Automatic Diabetic Retinopathy Classification with EfficientNet," IEEE REGION 10 CONFERENCE (TENCON), pp. 756-760, 2020, doi: 10.1109/TENCON50793.2020.9293941
5. M. A. Habib Raj, M. A. Mamun and M. F. Faruk, "CNN Based Diabetic Retinopathy Status Prediction Using Fundus Images," IEEE Region 10 Symposium (TENSYMP), pp. 190-193, 2020, doi: 10.1109/TENSYMP50017.2020.9230974.
6. Vaibhav V. Kamblea, Rajendra D. Kokate, “Automated diabetic retinopathy detection using
radial basis function,” Procedia Computer Science, vol. 167, pp. 799–808, 2020.
7. Alyoubi. W.L., Abulkhair. M.F. Shalash. W.M, “Diabetic Retinopathy Fundus Image Classification and Lesions Localization System Using Deep Learning,” Sensors, vol. 21, pp. 3704, 2021, https://doi.org/10.3390/s21113704
8. Mobeen-ur-Rehman, S. H. Khan, Z. Abbas and S. M. Danish Rizvi, "Classification of Diabetic Retinopathy Images Based on Customised CNN Architecture," Amity International Conference on Artificial Intelligence (AICAI), pp. 244-248, 2019, doi: 10.1109/AICAI.2019.8701231.
9. Supriya Mishra, Seema Hanchate, Zia Saquib, “Diabetic Retinopathy Detection using Deep Learning,” International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE, 2020.
10. R. Vidhya Lavanya, S. EP, C. Jayakumari and R. Isaac, "Detection and Classification of Diabetic Retinopathy using Raspberry PI," 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2020, pp. 1688-1691, doi: 10.1109/ICECA49313.2020.9297408.
11. Sinthanayothin. C, Boyce. F, Cook. H, “Automated detection of diabetic retinopathy on digital fundus images. Diabetic Medicine,” vol. 19, pp. 105-112, 2020.
12. Mehta. R, Majumdar. M, Sharma. A, “Classification of diabetic retinopathy images using multiwavelet transform and support vector machine,” Journal of Medical Systems, vol. 36, pp. 2099-2107, 2012.
13. Gulshan. V, Peng. L, Coram. M, Stumpe. M. C, Wu, Narayanaswamy. A, Webster. D. R, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” vol. 22, pp. 2402-2410, 2016.
14. Haloi. M, Bhattacharjee. D, Kalita. J. K, “Detection of diabetic retinopathy using support vector machine classification. Journal of medical systems,” vol. 40, pp. 1-11, 2016.
15. Quellec. G, Charrière. K, Boudi. Y, Cochener. B, Lamard. M, “Deep image mining for diabetic retinopathy screening,” Med Image Analytics, vol. 39, pp. 178-193, 2017.
16. Z. Qian, C. Wu. H. Chen, M. Chen, "Diabetic Retinopathy Grading Using Attention based Convolution Neural Network," IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), pp. 2652-2655, 2021, doi: 10.1109/IAEAC50856.2021.9390963.
17. H. Seetah, N. Singh, P. Meel and T. Dhudi, "A Convolutional Neural Network Approach to Diabetic Retinopathy Detection and its Automated Classification," 7th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 1000-1006, 2021 doi: 10.1109/ICACCS51430.2021.9441943.
18. A. Bilal, G. Sun, Y. Li, S. Mazhar, A. Q. Khan, "Diabetic Retinopathy Detection and Classification Using Mixed Models for a Disease Grading Database," IEEE Access, vol. 9, pp. 23544-23553, 2021, doi: 10.1109/ACCESS.2021.3056186.
19. Yuchen Wu, Ze Hu, “Recognition of Diabetic Retinopathy Based on Transfer Learning,” IEEE Cloud Computing and Big Data Analytics, 2019.
20. M. Kolla and V. T, "Efficient Classification of Diabetic Retinopathy using Binary CNN," International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), pp. 244-247, 2021, doi: 10.1109/ICCIKE51210.2021.9410719.
21. M. M. Shahriar Maswood, T. Hussain, M. B. Khan, M. T. Islam and A. G. Alharbi, "CNN Based Detection of the Severity of Diabetic Retinopathy from the Fundus Photography using EfficientNet-B5," 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON),2020, pp. 0147-0150, doi: 10.1109/IEMCON51383.2020.9284944.
22. D. U. N. Qomariah, H. Tjandrasa and C. Fatichah, "Classification of Diabetic Retinopathy and Normal Retinal Images using CNN and SVM," 12th International Conference on Information & Communication Technology and System (ICTS), 2019, pp. 152-157, doi: 10.1109/ICTS.2019.8850940.
23. Brahami Menaouer, Zoulikha Dermane, Nour El Houda Kebir, Nada Matta, “Diabetic Retinopathy Classifcation Using Hybrid Deep Learning Approach,” SN Computer Science, vol. 3, pp. 357, 2022, doi:10.1007/s42979-022-01240-8
24. S. Yu, D. Xiao and Y. Kanagasingam, "Machine Learning Based Automatic Neovascularization Detection on Optic Disc Region," IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 3, pp. 886-894, May 2018, doi: 10.1109/JBHI.2017.2710201.
25. Yasashvini R, Vergin Raja Sarobin M, Rukmani Panjanathan, Graceline Jasmine S, Jani Anbarasi L, “Diabetic Retinopathy Classification Using CNN and Hybrid Deep Convolutional Neural Networks,” Symmetry, vol. 14, 2022.
26. Revathy R, Nithya B, Reshma J, Ragendhu S, Sumithra M D, “Diabetic Retinopathy Detection using Machine Learning,” International Journal of Engineering Research & Technology, ISSN: 2278-0181, vol. 9, 2020, doi: IJERTV9IS060170.
27. Z. Gao, J. Li, J. Guo, Y. Chen, Z. Yi and J. Zhong, "Diagnosis of Diabetic Retinopathy Using Deep Neural Networks," IEEE Access, vol. 7, pp. 3360-3370, 2019, doi: 10.1109/ACCESS.2018.2888639.
28. Z. Fan et al., "Optic Disk Detection in Fundus Image Based on Structured Learning," IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 1, pp. 224-234, 2018, doi: 10.1109/JBHI.2017.2723678.
29. C. Agurto et al., "A Multiscale Optimization Approach to Detect Exudates in the Macula," IEEE Journal of Biomedical and Health Informatics, vol. 18, no. 4, pp. 1328-1336,2014, doi: 10.1109/JBHI.2013.2296399.
30. S. Kumar and B. Kumar, "Diabetic Retinopathy Detection by Extracting Area and Number of Microaneurysm from Colour Fundus Image," 5th International Conference on Signal Processing and Integrated Networks (SPIN), 2018, pp. 359-364, doi: 10.1109/SPIN.2018.8474264.
31. Muhammad Zubair, Umesh Kumar Naik M, Gunturi NVS Chandra Mouli, “Facile Diabetic Retinopathy Detection using MRHE-FEED and Classification using Deep Convolutional Neural Network,” IEEE International Conference on Industrial and Information System, vol. 978, no. 1, pp. 7281-8524, 2020.
2. P. W. Sudarmadji, P. DevianiPakan, R. YefrenesDillak, "Diabetic Retinopathy Stages Classification using Improved Deep Learning," International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS), pp. 104-109, 2020, doi: 10.1109/ICIMCIS51567.2020.9354281.
3. A. Lands, A. J. Kottarathil, A. Biju, E. M. Jacob and S. Thomas, "Implementation of deep learning based algorithms for diabetic retinopathy classification from fundus images," International Conference on Trends in Electronics and Informatics (ICOEI), pp. 1028-1032, 2020, doi: 10.1109/ICOEI48184.2020.9142878.
4. R. N. Lazuardi, N. Abiwinanda, T. H. Suryawan, M. Hanif and A. Handayani, "Automatic Diabetic Retinopathy Classification with EfficientNet," IEEE REGION 10 CONFERENCE (TENCON), pp. 756-760, 2020, doi: 10.1109/TENCON50793.2020.9293941
5. M. A. Habib Raj, M. A. Mamun and M. F. Faruk, "CNN Based Diabetic Retinopathy Status Prediction Using Fundus Images," IEEE Region 10 Symposium (TENSYMP), pp. 190-193, 2020, doi: 10.1109/TENSYMP50017.2020.9230974.
6. Vaibhav V. Kamblea, Rajendra D. Kokate, “Automated diabetic retinopathy detection using
radial basis function,” Procedia Computer Science, vol. 167, pp. 799–808, 2020.
7. Alyoubi. W.L., Abulkhair. M.F. Shalash. W.M, “Diabetic Retinopathy Fundus Image Classification and Lesions Localization System Using Deep Learning,” Sensors, vol. 21, pp. 3704, 2021, https://doi.org/10.3390/s21113704
8. Mobeen-ur-Rehman, S. H. Khan, Z. Abbas and S. M. Danish Rizvi, "Classification of Diabetic Retinopathy Images Based on Customised CNN Architecture," Amity International Conference on Artificial Intelligence (AICAI), pp. 244-248, 2019, doi: 10.1109/AICAI.2019.8701231.
9. Supriya Mishra, Seema Hanchate, Zia Saquib, “Diabetic Retinopathy Detection using Deep Learning,” International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE, 2020.
10. R. Vidhya Lavanya, S. EP, C. Jayakumari and R. Isaac, "Detection and Classification of Diabetic Retinopathy using Raspberry PI," 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2020, pp. 1688-1691, doi: 10.1109/ICECA49313.2020.9297408.
11. Sinthanayothin. C, Boyce. F, Cook. H, “Automated detection of diabetic retinopathy on digital fundus images. Diabetic Medicine,” vol. 19, pp. 105-112, 2020.
12. Mehta. R, Majumdar. M, Sharma. A, “Classification of diabetic retinopathy images using multiwavelet transform and support vector machine,” Journal of Medical Systems, vol. 36, pp. 2099-2107, 2012.
13. Gulshan. V, Peng. L, Coram. M, Stumpe. M. C, Wu, Narayanaswamy. A, Webster. D. R, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” vol. 22, pp. 2402-2410, 2016.
14. Haloi. M, Bhattacharjee. D, Kalita. J. K, “Detection of diabetic retinopathy using support vector machine classification. Journal of medical systems,” vol. 40, pp. 1-11, 2016.
15. Quellec. G, Charrière. K, Boudi. Y, Cochener. B, Lamard. M, “Deep image mining for diabetic retinopathy screening,” Med Image Analytics, vol. 39, pp. 178-193, 2017.
16. Z. Qian, C. Wu. H. Chen, M. Chen, "Diabetic Retinopathy Grading Using Attention based Convolution Neural Network," IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), pp. 2652-2655, 2021, doi: 10.1109/IAEAC50856.2021.9390963.
17. H. Seetah, N. Singh, P. Meel and T. Dhudi, "A Convolutional Neural Network Approach to Diabetic Retinopathy Detection and its Automated Classification," 7th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 1000-1006, 2021 doi: 10.1109/ICACCS51430.2021.9441943.
18. A. Bilal, G. Sun, Y. Li, S. Mazhar, A. Q. Khan, "Diabetic Retinopathy Detection and Classification Using Mixed Models for a Disease Grading Database," IEEE Access, vol. 9, pp. 23544-23553, 2021, doi: 10.1109/ACCESS.2021.3056186.
19. Yuchen Wu, Ze Hu, “Recognition of Diabetic Retinopathy Based on Transfer Learning,” IEEE Cloud Computing and Big Data Analytics, 2019.
20. M. Kolla and V. T, "Efficient Classification of Diabetic Retinopathy using Binary CNN," International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), pp. 244-247, 2021, doi: 10.1109/ICCIKE51210.2021.9410719.
21. M. M. Shahriar Maswood, T. Hussain, M. B. Khan, M. T. Islam and A. G. Alharbi, "CNN Based Detection of the Severity of Diabetic Retinopathy from the Fundus Photography using EfficientNet-B5," 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON),2020, pp. 0147-0150, doi: 10.1109/IEMCON51383.2020.9284944.
22. D. U. N. Qomariah, H. Tjandrasa and C. Fatichah, "Classification of Diabetic Retinopathy and Normal Retinal Images using CNN and SVM," 12th International Conference on Information & Communication Technology and System (ICTS), 2019, pp. 152-157, doi: 10.1109/ICTS.2019.8850940.
23. Brahami Menaouer, Zoulikha Dermane, Nour El Houda Kebir, Nada Matta, “Diabetic Retinopathy Classifcation Using Hybrid Deep Learning Approach,” SN Computer Science, vol. 3, pp. 357, 2022, doi:10.1007/s42979-022-01240-8
24. S. Yu, D. Xiao and Y. Kanagasingam, "Machine Learning Based Automatic Neovascularization Detection on Optic Disc Region," IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 3, pp. 886-894, May 2018, doi: 10.1109/JBHI.2017.2710201.
25. Yasashvini R, Vergin Raja Sarobin M, Rukmani Panjanathan, Graceline Jasmine S, Jani Anbarasi L, “Diabetic Retinopathy Classification Using CNN and Hybrid Deep Convolutional Neural Networks,” Symmetry, vol. 14, 2022.
26. Revathy R, Nithya B, Reshma J, Ragendhu S, Sumithra M D, “Diabetic Retinopathy Detection using Machine Learning,” International Journal of Engineering Research & Technology, ISSN: 2278-0181, vol. 9, 2020, doi: IJERTV9IS060170.
27. Z. Gao, J. Li, J. Guo, Y. Chen, Z. Yi and J. Zhong, "Diagnosis of Diabetic Retinopathy Using Deep Neural Networks," IEEE Access, vol. 7, pp. 3360-3370, 2019, doi: 10.1109/ACCESS.2018.2888639.
28. Z. Fan et al., "Optic Disk Detection in Fundus Image Based on Structured Learning," IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 1, pp. 224-234, 2018, doi: 10.1109/JBHI.2017.2723678.
29. C. Agurto et al., "A Multiscale Optimization Approach to Detect Exudates in the Macula," IEEE Journal of Biomedical and Health Informatics, vol. 18, no. 4, pp. 1328-1336,2014, doi: 10.1109/JBHI.2013.2296399.
30. S. Kumar and B. Kumar, "Diabetic Retinopathy Detection by Extracting Area and Number of Microaneurysm from Colour Fundus Image," 5th International Conference on Signal Processing and Integrated Networks (SPIN), 2018, pp. 359-364, doi: 10.1109/SPIN.2018.8474264.
31. Muhammad Zubair, Umesh Kumar Naik M, Gunturi NVS Chandra Mouli, “Facile Diabetic Retinopathy Detection using MRHE-FEED and Classification using Deep Convolutional Neural Network,” IEEE International Conference on Industrial and Information System, vol. 978, no. 1, pp. 7281-8524, 2020.
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Published
May 18, 2023
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How to Cite
J. Pradeep, N. Erick Jeffery, M. Saranraj, & J. Nasser Hussain. (2023). Enhanced Recognition system for Diabetic Retinopathy using Machine Learning with Deep Learning Approach. Journal of Population Therapeutics and Clinical Pharmacology, 30(11), 452–466. https://doi.org/10.47750/jptcp.2023.30.11.047
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