An Enhanced Convolutional Neural Network (CNN) based P-EDR Mechanism for Diagnosis of Diabetic Retinopathy (DR) using Machine Learning
Received: 30 August 2024 | Revised: 23 September 2024 | Accepted: 27 September 2024 | Online: 23 November 2024
Corresponding author: Hamayun Khan
Abstract
This study focuses on Diabetic Retinopathy (DR), a disease caused by diabetes that affects the retina of the eye and eventually leads to blindness. Diabetes development progresses to retinopathy and must be addressed at an early stage for effective treatment. Currently, DR is classified as Non-Proliferative DR (NPDR) and Proliferative DR (PDR). This study proposes an Enhanced DR (P-EDR) method based on CNN using a high-resolution dataset benchmark of retinal images. Initially, the data were preprocessed by normalization, augmentation, and resizing to improve image quality and feature extraction. Evaluation was based on accuracy, specificity, sensitivity, and AUC-ROC. The proposed CNN-based P-EDR outperformed advanced ML strategies such as Support Vector Machine (SVM), Random Forest (RF), Probabilistic Neural network (PNN), and Gradient Boosting Machine (GBM) that were executed and compared to diagnose and classify DR. The proposed P-EDR extracts features such as a hemorrhage of the NPDR retina image to identify the disease using image processing for classification. P-EDR provides significant features from images in detection and classification, making it a successful model for diagnosing DR with improved accuracy of 93%, sensitivity of 92%, specificity of 94%, and AUC-ROC of 0.97%. These results highlight the potential of a P-EDR-based machine learning model to support ophthalmologists with the early and precise detection of DR, eventually helping with appropriate treatment and prevention of vision loss.
Keywords:
diabetic retinopathy, machine learning, convolutional neural networks, support vector machines, random forest, gradient boosting machines, medical image analysis, Non-Proliferative Diabetic Retinopathy (NPDR), Proliferative Diabetic Retinopathy (PDR)Downloads
References
R. Ramesh and S. Sathiamoorthy, "A Deep Learning Grading Classification of Diabetic Retinopathy on Retinal Fundus Images with Bio-inspired Optimization," Engineering, Technology & Applied Science Research, vol. 13, no. 4, pp. 11248–11252, Aug. 2023.
C. Nithyeswari and G. Karthikeyan, "An Effective Heuristic Optimizer with Deep Learning-assisted Diabetic Retinopathy Diagnosis on Retinal Fundus Images," Engineering, Technology & Applied Science Research, vol. 14, no. 3, pp. 14308–14312, Jun. 2024.
G. Swapna, R. Vinayakumar, and K. P. Soman, "Diabetes detection using deep learning algorithms," ICT Express, vol. 4, no. 4, pp. 243–246, Dec. 2018.
S. Gujral, "Early Diabetes Detection using Machine Learning: A Review," International Journal for Innovative Research in Science & Technology, vol. 3, no. 10, pp. 54–62, 2016.
T. Sharma and M. Shah, "A comprehensive review of machine learning techniques on diabetes detection," Visual Computing for Industry, Biomedicine, and Art, vol. 4, no. 1, Dec. 2021, Art. no. 30.
A. Mujumdar and V. Vaidehi, "Diabetes Prediction using Machine Learning Algorithms," Procedia Computer Science, vol. 165, pp. 292–299, Jan. 2019.
B. Farajollahi, M. Mehmannavaz, H. Mehrjoo, F. Moghbeli, and M. J. Sayadi, "Diabetes diagnosis using machine learning," Frontiers in Health Informatics, vol. 10, no. 1, Feb. 2021, Art. no. 65.
R. Katarya and S. Jain, "Comparison of Different Machine Learning Models for diabetes detection," in 2020 IEEE International Conference on Advances and Developments in Electrical and Electronics Engineering (ICADEE), Coimbatore, India, Dec. 2020, pp. 1–5.
M. Warke, V. Kumar, S. Tarale, P. Galgat, and D. J. Chaudhari, "Diabetes Diagnosis using Machine Learning Algorithms," International Research Journal of Engineering and Technology, vol. 06, no. 03, 2019.
T. Zhu, K. Li, P. Herrero, and P. Georgiou, "Deep Learning for Diabetes: A Systematic Review," IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 7, pp. 2744–2757, Jul. 2021.
A. H. Syed and T. Khan, "Machine Learning-Based Application for Predicting Risk of Type 2 Diabetes Mellitus (T2DM) in Saudi Arabia: A Retrospective Cross-Sectional Study," IEEE Access, vol. 8, pp. 199539–199561, 2020.
A. Sarwar, M. Ali, J. Manhas, and V. Sharma, "Diagnosis of diabetes type-II using hybrid machine learning based ensemble model," International Journal of Information Technology, vol. 12, no. 2, pp. 419–428, Jun. 2020.
H. Lai, H. Huang, K. Keshavjee, A. Guergachi, and X. Gao, "Predictive models for diabetes mellitus using machine learning techniques," BMC Endocrine Disorders, vol. 19, no. 1, Oct. 2019, Art. no. 101.
A. S. Alenizi and K. A. Al-karawi, "Machine Learning Approach for Diabetes Prediction," in Proceedings of Eighth International Congress on Information and Communication Technology, London, UK, 2024, pp. 745–756.
G. Bhuvaneswari and G. Manikandan, "A novel machine learning framework for diagnosing the type 2 diabetics using temporal fuzzy ant miner decision tree classifier with temporal weighted genetic algorithm," Computing, vol. 100, no. 8, pp. 759–772, Aug. 2018.
L. Math and R. Fatima, "Adaptive machine learning classification for diabetic retinopathy," Multimedia Tools and Applications, vol. 80, no. 4, pp. 5173–5186, Feb. 2021.
T. Sharma and M. Shah, "A comprehensive review of machine learning techniques on diabetes detection," Visual Computing for Industry, Biomedicine, and Art, vol. 4, no. 1, Dec. 2021, Art. no. 30.
U. M. Butt, S. Letchmunan, M. Ali, F. H. Hassan, A. Baqir, and H. H. R. Sherazi, "Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications," Journal of Healthcare Engineering, vol. 2021, no. 1, 2021, Art. no. 9930985.
S. S. Bhat, V. Selvam, G. A. Ansari, M. D. Ansari, and M. H. Rahman, "Prevalence and Early Prediction of Diabetes Using Machine Learning in North Kashmir: A Case Study of District Bandipora," Computational Intelligence and Neuroscience, vol. 2022, no. 1, 2022, Art. no. 2789760.
E. Dugas, J. Jared, and W. Cukierski, "Diabetic Retinopathy Detection." https://kaggle.com/diabetic-retinopathy-detection.
Downloads
How to Cite
License
Copyright (c) 2024 Munawar Hussain, Hassan A. Ahmed, Muhammad Zeeshan Babar, Arshad Ali, H. M. Shahzad, Saif ur Rehman, Hamayun Khan, Abdulaziz M. Alshahrani
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
