Treffer: Coinciding Diabetic Retinopathy and Diabetic Macular Edema Grading With Rat Swarm Optimization Algorithm for Enhanced Capsule Generation Adversarial Network.
Title:
Coinciding Diabetic Retinopathy and Diabetic Macular Edema Grading With Rat Swarm Optimization Algorithm for Enhanced Capsule Generation Adversarial Network.
Authors:
Ramshankar N; Department of Computer Science and Engineering, R.M.D. Engineering College, Tiruvallur, Tamil Nadu, India., Murugesan S; Department of Computer Science and Engineering, R.M.D. Engineering College, Tiruvallur, Tamil Nadu, India., K V P; Department of Information Technology, St. Peter's College of Engineering and Technology, Avadi, Tamil Nadu, India., Prathap PMJ; Department of Computer Science and Engineering, R.M.D. Engineering College, Tiruvallur, Tamil Nadu, India.
Source:
Microscopy research and technique [Microsc Res Tech] 2025 Feb; Vol. 88 (2), pp. 555-563. Date of Electronic Publication: 2024 Nov 02.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Wiley-Liss Country of Publication: United States NLM ID: 9203012 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-0029 (Electronic) Linking ISSN: 1059910X NLM ISO Abbreviation: Microsc Res Tech Subsets: MEDLINE
Imprint Name(s):
Original Publication: New York, NY : Wiley-Liss, c1992-
MeSH Terms:
References:
Abbas, Q., I. Qureshi, J. Yan, and K. Shaheed. 2022. “Machine Learning Methods for Diagnosis of Eye‐Related Diseases: A Systematic Review Study Based on Ophthalmic Imaging Modalities.” Archives of Computational Methods in Engineering 29, no. 6: 3861–3918.
Anish, T. P., and P. J. Prathap. 2024. “A Novel Approach for Multi‐CHD Prediction Using IW‐LNEF‐DJRNN Model Based on 3D CT Images.” Biomedical Signal Processing and Control 92: 106074.
Ashwini, K., and R. Dash. 2023. “Grading Diabetic Retinopathy Using Multiresolution Based CNN.” Biomedical Signal Processing and Control 86: 105210.
Beham, A. R., and V. Thanikaiselvan. 2023. “An Optimized Deep‐Learning Algorithm for the Automated Detection of Diabetic Retinopathy.” Soft Computing: 1–11.
Bhimavarapu, U. 2024. “Diagnosis and Multiclass Classification of Diabetic Retinopathy Using Enhanced Multi Thresholding Optimization Algorithms and Improved Naive Bayes Classifier.” Multimedia Tools and Applications: 1–35.
Bosma, E. K., C. J. van Noorden, I. Klaassen, and R. O. Schlingemann. 2019. Diabetic Nephropathy, 305–321. New York: Springer.
Cao, P., Q. Hou, R. Song, H. Wang, and O. Zaiane. 2022. “Collaborative Learning of Weakly‐Supervised Domain Adaptation for Diabetic Retinopathy Grading on Retinal Images.” Computers in Biology and Medicine 144: 105341.
Cazañas‐Gordón, A., and L. A. da Silva Cruz. 2022. “Multiscale Attention Gated Network (MAGNet) for Retinal Layer and Macular Cystoid Edema Segmentation.” IEEE Access 10: 85905–85917.
Chandran, J. J. G., J. Jabez, and S. Srinivasulu. 2023. “Auto‐Metric Graph Neural Network Optimized With Capuchin Search Optimization Algorithm for Coinciding Diabetic Retinopathy and Diabetic Macular Edema Grading.” Biomedical Signal Processing and Control 80: 104386.
Chatziralli, I. 2021. “Ranibizumab for the Treatment of Diabetic Retinopathy.” Expert Opinion on Biological Therapy 21, no. 8: 991–997.
Chaudhary, P. K., and R. B. Pachori. 2022. “Automatic Diagnosis of Different Grades of Diabetic Retinopathy and Diabetic Macular Edema Using 2‐D‐FBSE‐FAWT.” IEEE Transactions on Instrumentation and Measurement 71: 1–9.
Date, R. C., K. L. Shen, B. M. Shah, M. A. Sigalos‐Rivera, Y. I. Chu, and C. Y. Weng. 2019. “Accuracy of Detection and Grading of Diabetic Retinopathy and Diabetic Macular Edema Using Teleretinal Screening.” Ophthalmology Retina 3, no. 4: 343–349.
Gomathi, P., C. Muniraj, and P. S. Periasamy. 2023. “Digital Infrared Thermal Imaging System Based Breast Cancer Diagnosis Using 4D U‐Net Segmentation.” Biomedical Signal Processing and Control 85: 104792.
Guefrachi, S., A. Echtioui, and H. Hamam. 2024. “Automated Diabetic Retinopathy Screening Using Deep Learning.” Multimedia Tools and Applications 83: 1–18.
He, A., T. Li, N. Li, K. Wang, and H. Fu. 2020. “CABNet: Category Attention Block for Imbalanced Diabetic Retinopathy Grading.” IEEE Transactions on Medical Imaging 40, no. 1: 143–153. https://ieee‐dataport.org/open‐access/indian‐diabetic‐retinopathy‐image‐dataset‐idrid.
Jacoba, C. M. P., D. Doan, R. P. Salongcay, et al. 2023. “Performance of Automated Machine Learning for Diabetic Retinopathy Image Classification From Multi‐Field Handheld Retinal Images.” Ophthalmology Retina 7, no. 8: 703–712.
Khaparde, A., S. Chapadgaonkar, M. Kowdiki, and V. Deshmukh. 2023. “An Attention‐Based Swin U‐Net‐Based Segmentation and Hybrid Deep Learning Based Diabetic Retinopathy Classification Framework Using Fundus Images.” Sensing and Imaging 24, no. 1: 20.
Kwan, C. C., and A. A. Fawzi. 2019. “Imaging and Biomarkers in Diabetic Macular Edema and Diabetic Retinopathy.” Current Diabetes Reports 19: 1–10.
Li, X., X. Hu, L. Yu, L. Zhu, C. W. Fu, and P. A. Heng. 2019. “CANet: Cross‐Disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading.” IEEE Transactions on Medical Imaging 39, no. 5: 1483–1493.
Lyu, Z., C. Zhang, and M. Han. 2021. “DSTnet: A New Discrete Shearlet Transform‐Based CNN Model for Image Denoising.” Multimedia Systems 27: 1165–1177.
Mathews, M. R., and S. M. Anzar. 2021. “A Comprehensive Review on Automated Systems for Severity Grading of Diabetic Retinopathy and Macular Edema.” International Journal of Imaging Systems and Technology 31, no. 4: 2093–2122.
Nagarathna, C. R., M. Kusuma, and K. Seemanthini. 2023. “Classifying the stages of Alzheimers disease by using multi layer feed forward neural network.” Procedia Computer Science 218: 1845–1856.
Noguera, J. L. V., J. C. Mello‐Román, D. P. Pinto‐Roa, et al. 2023. “Multiclass Diabetic Retinopathy Classification of Eye Fundus Images Small Datasets Performance Improvement–A Neuroevolution Approach.” In 2023 XLIX Latin American Computer Conference (CLEI), 1–10. IEEE.
Porwal, P., S. Pachade, R. Kamble, et al. 2018. “Diabetic Retinopathy: Segmentation and Grading Challenge Workshop.” In Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI), 1.
Reddy, V. P. C., and K. K. Gurrala. 2022. “Joint DR‐DME Classification Using Deep Learning‐CNN Based Modified Grey‐Wolf Optimizer With Variable Weights.” Biomedical Signal Processing and Control 73: 103439.
Shajin, F. H., B. Aruna Devi, N. B. Prakash, G. R. Sreekanth, and P. Rajesh. 2023. “Sailfish Optimizer With Levy Flight, Chaotic and Opposition‐Based Multi‐Level Thresholding for Medical Image Segmentation.” Soft Computing 27: 1–26.
Shajin, F. H., P. Rajesh, and M. R. Raja. 2022. “An Efficient VLSI Architecture for Fast Motion Estimation Exploiting Zero Motion Prejudgment Technique and a New Quadrant‐Based Search Algorithm in HEVC.” Circuits, Systems, and Signal Processing: 1–24.
Shamsolmoali, P., M. Zareapoor, L. Shen, A. H. Sadka, and J. Yang. 2021. “Imbalanced Data Learning by Minority Class Augmentation Using Capsule Adversarial Networks.” Neurocomputing 459: 481–493.
Singla, M. K., P. Nijhawan, and A. S. Oberoi. 2022. “A Novel Hybrid Particle Swarm Optimization Rat Search Algorithm for Parameter Estimation of Solar PV and Fuel Cell Model.” COMPEL‐The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 41, no. 5: 1505–1527.
Sivapriya, G., R. M. Devi, P. Keerthika, and V. Praveen. 2024. “Automated Diagnostic Classification of Diabetic Retinopathy With Microvascular Structure of Fundus Images Using Deep Learning Method.” Biomedical Signal Processing and Control 88: 105616.
Steinmetz, J. D., R. R. Bourne, P. S. Briant, et al. 2021. “Causes of Blindness and Vision Impairment in 2020 and Trends Over 30 Years, and Prevalence of Avoidable Blindness in Relation to VISION 2020: The Right to Sight: An Analysis for the Global Burden of Disease Study.” Lancet Global Health 9, no. 2: e144–e160.
Suresh, T., Z. Brijet, and T. D. Subha. 2023. “Imbalanced Medical Disease Dataset Classification Using Enhanced Generative Adversarial Network.” Computer Methods in Biomechanics and Biomedical Engineering 26, no. 14: 1702–1718.
Suriyasekeran, K., S. Santhanamahalingam, and M. Duraisamy. 2021. “Algorithms for Diagnosis of Diabetic Retinopathy and Diabetic Macula Edema‐A Review.” Diabetes: From Research to Clinical Practice 4: 357–373.
Tan, Z., G. De, M. Li, et al. 2020. “Combined Electricity‐Heat‐Cooling‐Gas Load Forecasting Model for Integrated Energy System Based on Multi‐Task Learning and Least Square Support Vector Machine.” Journal of Cleaner Production 248: 119252.
Thulkar, D., R. Daruwala, and N. Sardar. 2020. “An Integrated System for Detection Exudates and Severity Quantification for Diabetic Macular Edema.” Journal of Medical and Biological Engineering 40: 798–820.
Vijayalakshmi, J., and E. Ramaraj. 2022. “A Hadoop‐Big Data Analytic Model to Predict and Classify Chronic Kidney Diseases Using Improved Fractional Rough Fuzzy K‐Means Clustering and Extreme Gradient Boost Rat Swarm Optimizer.” Concurrency and Computation: Practice and Experience 34, no. 28: e7354.
Wang, M., T. Lin, Y. Peng, et al. 2023. “Self‐Guided Optimization Semi‐Supervised Method for Joint Segmentation of Macular Hole and Cystoid Macular Edema in Retinal OCT Images.” IEEE Transactions on Biomedical Engineering 70, no. 7: 2013–2024.
Wang, X., H. Chen, A. R. Ran, et al. 2020. “Towards Multi‐Center Glaucoma OCT Image Screening With Semi‐Supervised Joint Structure and Function Multi‐Task Learning.” Medical Image Analysis 63: 101695.
Yang, H., Y. Cheng, and G. Li. 2021. “A Denoising Method for Ship Radiated Noise Based on Spearman Variational Mode Decomposition, Spatial‐Dependence Recurrence Sample Entropy, Improved Wavelet Threshold Denoising, and Savitzky‐Golay Filter.” Alexandria Engineering Journal 60, no. 3: 3379–3400.
Zeng, N., H. Li, and Y. Peng. 2023. “A New Deep Belief Network‐Based Multi‐Task Learning for Diagnosis of Alzheimer's Disease.” Neural Computing and Applications 35, no. 16: 11599–11610.
Zhang, W., G. Yang, N. Zhang, et al. 2021. “Multi‐Task Learning With Multi‐View Weighted Fusion Attention for Artery‐Specific Calcification Analysis.” Information Fusion 71: 64–76.
Zhao, J., B. Du, L. Sun, W. Lv, Y. Liu, and H. Xiong. 2021. “Deep Multi‐Task Learning With Relational Attention for Business Success Prediction.” Pattern Recognition 110: 107469.
Anish, T. P., and P. J. Prathap. 2024. “A Novel Approach for Multi‐CHD Prediction Using IW‐LNEF‐DJRNN Model Based on 3D CT Images.” Biomedical Signal Processing and Control 92: 106074.
Ashwini, K., and R. Dash. 2023. “Grading Diabetic Retinopathy Using Multiresolution Based CNN.” Biomedical Signal Processing and Control 86: 105210.
Beham, A. R., and V. Thanikaiselvan. 2023. “An Optimized Deep‐Learning Algorithm for the Automated Detection of Diabetic Retinopathy.” Soft Computing: 1–11.
Bhimavarapu, U. 2024. “Diagnosis and Multiclass Classification of Diabetic Retinopathy Using Enhanced Multi Thresholding Optimization Algorithms and Improved Naive Bayes Classifier.” Multimedia Tools and Applications: 1–35.
Bosma, E. K., C. J. van Noorden, I. Klaassen, and R. O. Schlingemann. 2019. Diabetic Nephropathy, 305–321. New York: Springer.
Cao, P., Q. Hou, R. Song, H. Wang, and O. Zaiane. 2022. “Collaborative Learning of Weakly‐Supervised Domain Adaptation for Diabetic Retinopathy Grading on Retinal Images.” Computers in Biology and Medicine 144: 105341.
Cazañas‐Gordón, A., and L. A. da Silva Cruz. 2022. “Multiscale Attention Gated Network (MAGNet) for Retinal Layer and Macular Cystoid Edema Segmentation.” IEEE Access 10: 85905–85917.
Chandran, J. J. G., J. Jabez, and S. Srinivasulu. 2023. “Auto‐Metric Graph Neural Network Optimized With Capuchin Search Optimization Algorithm for Coinciding Diabetic Retinopathy and Diabetic Macular Edema Grading.” Biomedical Signal Processing and Control 80: 104386.
Chatziralli, I. 2021. “Ranibizumab for the Treatment of Diabetic Retinopathy.” Expert Opinion on Biological Therapy 21, no. 8: 991–997.
Chaudhary, P. K., and R. B. Pachori. 2022. “Automatic Diagnosis of Different Grades of Diabetic Retinopathy and Diabetic Macular Edema Using 2‐D‐FBSE‐FAWT.” IEEE Transactions on Instrumentation and Measurement 71: 1–9.
Date, R. C., K. L. Shen, B. M. Shah, M. A. Sigalos‐Rivera, Y. I. Chu, and C. Y. Weng. 2019. “Accuracy of Detection and Grading of Diabetic Retinopathy and Diabetic Macular Edema Using Teleretinal Screening.” Ophthalmology Retina 3, no. 4: 343–349.
Gomathi, P., C. Muniraj, and P. S. Periasamy. 2023. “Digital Infrared Thermal Imaging System Based Breast Cancer Diagnosis Using 4D U‐Net Segmentation.” Biomedical Signal Processing and Control 85: 104792.
Guefrachi, S., A. Echtioui, and H. Hamam. 2024. “Automated Diabetic Retinopathy Screening Using Deep Learning.” Multimedia Tools and Applications 83: 1–18.
He, A., T. Li, N. Li, K. Wang, and H. Fu. 2020. “CABNet: Category Attention Block for Imbalanced Diabetic Retinopathy Grading.” IEEE Transactions on Medical Imaging 40, no. 1: 143–153. https://ieee‐dataport.org/open‐access/indian‐diabetic‐retinopathy‐image‐dataset‐idrid.
Jacoba, C. M. P., D. Doan, R. P. Salongcay, et al. 2023. “Performance of Automated Machine Learning for Diabetic Retinopathy Image Classification From Multi‐Field Handheld Retinal Images.” Ophthalmology Retina 7, no. 8: 703–712.
Khaparde, A., S. Chapadgaonkar, M. Kowdiki, and V. Deshmukh. 2023. “An Attention‐Based Swin U‐Net‐Based Segmentation and Hybrid Deep Learning Based Diabetic Retinopathy Classification Framework Using Fundus Images.” Sensing and Imaging 24, no. 1: 20.
Kwan, C. C., and A. A. Fawzi. 2019. “Imaging and Biomarkers in Diabetic Macular Edema and Diabetic Retinopathy.” Current Diabetes Reports 19: 1–10.
Li, X., X. Hu, L. Yu, L. Zhu, C. W. Fu, and P. A. Heng. 2019. “CANet: Cross‐Disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading.” IEEE Transactions on Medical Imaging 39, no. 5: 1483–1493.
Lyu, Z., C. Zhang, and M. Han. 2021. “DSTnet: A New Discrete Shearlet Transform‐Based CNN Model for Image Denoising.” Multimedia Systems 27: 1165–1177.
Mathews, M. R., and S. M. Anzar. 2021. “A Comprehensive Review on Automated Systems for Severity Grading of Diabetic Retinopathy and Macular Edema.” International Journal of Imaging Systems and Technology 31, no. 4: 2093–2122.
Nagarathna, C. R., M. Kusuma, and K. Seemanthini. 2023. “Classifying the stages of Alzheimers disease by using multi layer feed forward neural network.” Procedia Computer Science 218: 1845–1856.
Noguera, J. L. V., J. C. Mello‐Román, D. P. Pinto‐Roa, et al. 2023. “Multiclass Diabetic Retinopathy Classification of Eye Fundus Images Small Datasets Performance Improvement–A Neuroevolution Approach.” In 2023 XLIX Latin American Computer Conference (CLEI), 1–10. IEEE.
Porwal, P., S. Pachade, R. Kamble, et al. 2018. “Diabetic Retinopathy: Segmentation and Grading Challenge Workshop.” In Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI), 1.
Reddy, V. P. C., and K. K. Gurrala. 2022. “Joint DR‐DME Classification Using Deep Learning‐CNN Based Modified Grey‐Wolf Optimizer With Variable Weights.” Biomedical Signal Processing and Control 73: 103439.
Shajin, F. H., B. Aruna Devi, N. B. Prakash, G. R. Sreekanth, and P. Rajesh. 2023. “Sailfish Optimizer With Levy Flight, Chaotic and Opposition‐Based Multi‐Level Thresholding for Medical Image Segmentation.” Soft Computing 27: 1–26.
Shajin, F. H., P. Rajesh, and M. R. Raja. 2022. “An Efficient VLSI Architecture for Fast Motion Estimation Exploiting Zero Motion Prejudgment Technique and a New Quadrant‐Based Search Algorithm in HEVC.” Circuits, Systems, and Signal Processing: 1–24.
Shamsolmoali, P., M. Zareapoor, L. Shen, A. H. Sadka, and J. Yang. 2021. “Imbalanced Data Learning by Minority Class Augmentation Using Capsule Adversarial Networks.” Neurocomputing 459: 481–493.
Singla, M. K., P. Nijhawan, and A. S. Oberoi. 2022. “A Novel Hybrid Particle Swarm Optimization Rat Search Algorithm for Parameter Estimation of Solar PV and Fuel Cell Model.” COMPEL‐The International Journal for Computation and Mathematics in Electrical and Electronic Engineering 41, no. 5: 1505–1527.
Sivapriya, G., R. M. Devi, P. Keerthika, and V. Praveen. 2024. “Automated Diagnostic Classification of Diabetic Retinopathy With Microvascular Structure of Fundus Images Using Deep Learning Method.” Biomedical Signal Processing and Control 88: 105616.
Steinmetz, J. D., R. R. Bourne, P. S. Briant, et al. 2021. “Causes of Blindness and Vision Impairment in 2020 and Trends Over 30 Years, and Prevalence of Avoidable Blindness in Relation to VISION 2020: The Right to Sight: An Analysis for the Global Burden of Disease Study.” Lancet Global Health 9, no. 2: e144–e160.
Suresh, T., Z. Brijet, and T. D. Subha. 2023. “Imbalanced Medical Disease Dataset Classification Using Enhanced Generative Adversarial Network.” Computer Methods in Biomechanics and Biomedical Engineering 26, no. 14: 1702–1718.
Suriyasekeran, K., S. Santhanamahalingam, and M. Duraisamy. 2021. “Algorithms for Diagnosis of Diabetic Retinopathy and Diabetic Macula Edema‐A Review.” Diabetes: From Research to Clinical Practice 4: 357–373.
Tan, Z., G. De, M. Li, et al. 2020. “Combined Electricity‐Heat‐Cooling‐Gas Load Forecasting Model for Integrated Energy System Based on Multi‐Task Learning and Least Square Support Vector Machine.” Journal of Cleaner Production 248: 119252.
Thulkar, D., R. Daruwala, and N. Sardar. 2020. “An Integrated System for Detection Exudates and Severity Quantification for Diabetic Macular Edema.” Journal of Medical and Biological Engineering 40: 798–820.
Vijayalakshmi, J., and E. Ramaraj. 2022. “A Hadoop‐Big Data Analytic Model to Predict and Classify Chronic Kidney Diseases Using Improved Fractional Rough Fuzzy K‐Means Clustering and Extreme Gradient Boost Rat Swarm Optimizer.” Concurrency and Computation: Practice and Experience 34, no. 28: e7354.
Wang, M., T. Lin, Y. Peng, et al. 2023. “Self‐Guided Optimization Semi‐Supervised Method for Joint Segmentation of Macular Hole and Cystoid Macular Edema in Retinal OCT Images.” IEEE Transactions on Biomedical Engineering 70, no. 7: 2013–2024.
Wang, X., H. Chen, A. R. Ran, et al. 2020. “Towards Multi‐Center Glaucoma OCT Image Screening With Semi‐Supervised Joint Structure and Function Multi‐Task Learning.” Medical Image Analysis 63: 101695.
Yang, H., Y. Cheng, and G. Li. 2021. “A Denoising Method for Ship Radiated Noise Based on Spearman Variational Mode Decomposition, Spatial‐Dependence Recurrence Sample Entropy, Improved Wavelet Threshold Denoising, and Savitzky‐Golay Filter.” Alexandria Engineering Journal 60, no. 3: 3379–3400.
Zeng, N., H. Li, and Y. Peng. 2023. “A New Deep Belief Network‐Based Multi‐Task Learning for Diagnosis of Alzheimer's Disease.” Neural Computing and Applications 35, no. 16: 11599–11610.
Zhang, W., G. Yang, N. Zhang, et al. 2021. “Multi‐Task Learning With Multi‐View Weighted Fusion Attention for Artery‐Specific Calcification Analysis.” Information Fusion 71: 64–76.
Zhao, J., B. Du, L. Sun, W. Lv, Y. Liu, and H. Xiong. 2021. “Deep Multi‐Task Learning With Relational Attention for Business Success Prediction.” Pattern Recognition 110: 107469.
Contributed Indexing:
Keywords: diabetic macular edema grading; diabetic retinopathy; enhanced capsule generation adversarial network; rat swarm optimization
Entry Date(s):
Date Created: 20241102 Date Completed: 20250425 Latest Revision: 20250618
Update Code:
20250618
DOI:
10.1002/jemt.24709
PMID:
39487733
Database:
MEDLINE
Weitere Informationen
In the worldwide working-age population, visual disability and blindness are common conditions caused by diabetic retinopathy (DR) and diabetic macular edema (DME). Nowadays, due to diabetes, many people are affected by eye-related issues. Among these, DR and DME are the two foremost eye diseases, the severity of which may lead to some eye-related problems and blindness. Early detection of DR and DME is essential to preventing vision loss. Therefore, an enhanced capsule generation adversarial network (ECGAN) optimized with the rat swarm optimization (RSO) approach is proposed in this article to coincide with DR and DME grading (DR-DME-ECGAN-RSO-ISBI 2018 IDRiD). The input images are obtained from the ISBI 2018 unbalanced DR grading data set. Then, the input fundus images are preprocessed using the Savitzky-Golay (SG) filter filtering technique, which reduces noise from the input image. The preprocessed image is fed to the discrete shearlet transform (DST) for feature extraction. The extracting features of DR-DME are given to the ECGAN-RSO algorithm to categorize the grading of DR and DME disorders. The proposed approach is implemented in Python and achieves better accuracy by 7.94%, 36.66%, and 4.88% compared to the existing models, such as the combined DR with DME grading for the cross-disease attention network (DR-DME-CANet-ISBI 2018 IDRiD), category attention block for unbalanced grading of DR (DR-DME-HDLCNN-MGMO-ISBI 2018 IDRiD), combined DR-DME classification with a deep learning-convolutional neural network-based modified gray-wolf optimizer with variable weights (DR-DME-ANN-ISBI 2018 IDRiD).
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