Triple-Stream Deep Feature Selection with Metaheuristic Optimization and Machine Learning for Multi-Stage Hypertensive Retinopathy Diagnosis
Hypertensive retinopathy (HR) is a serious eye disease that can lead to permanent vision loss if not diagnosed early. The conventional diagnostic methods are subjective and time-consuming, so there is a need for an automated and reliable system. In this study, a three-stage method that provides high...
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MDPI AG
2025-06-01
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| author | Süleyman Burçin Şüyun Mustafa Yurdakul Şakir Taşdemir Serkan Biliş |
| author_facet | Süleyman Burçin Şüyun Mustafa Yurdakul Şakir Taşdemir Serkan Biliş |
| author_sort | Süleyman Burçin Şüyun |
| collection | DOAJ |
| description | Hypertensive retinopathy (HR) is a serious eye disease that can lead to permanent vision loss if not diagnosed early. The conventional diagnostic methods are subjective and time-consuming, so there is a need for an automated and reliable system. In this study, a three-stage method that provides high accuracy in HR diagnosis is proposed. In the first stage, 14 well-known Convolutional Neural Network (CNN) models were evaluated, and the top three models were identified. Among these models, DenseNet169 achieved the highest accuracy rate of 87.73%. In the second stage, the deep features obtained from these three models were combined and classified using machine learning (ML) algorithms including Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The SVM with a sigmoid kernel achieved the best performance (92% accuracy). In the third stage, feature selection was performed using metaheuristic optimization techniques including Genetic Algorithm (GA), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Harris Hawk Optimization (HHO). The HHO algorithm increased the classification accuracy to 94.66%, enhancing the model’s generalization ability and reducing misclassifications. The proposed method provides superior accuracy in the diagnosis of HR at different severity levels compared to single-model CNN approaches. These results demonstrate that the integration of Deep Learning (DL), ML, and optimization techniques holds significant potential in automated HR diagnosis. |
| format | Article |
| id | doaj-art-2d76b4c1fde94ea29ff04ced8aecfaa4 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-2d76b4c1fde94ea29ff04ced8aecfaa42025-08-20T02:24:18ZengMDPI AGApplied Sciences2076-34172025-06-011512648510.3390/app15126485Triple-Stream Deep Feature Selection with Metaheuristic Optimization and Machine Learning for Multi-Stage Hypertensive Retinopathy DiagnosisSüleyman Burçin Şüyun0Mustafa Yurdakul1Şakir Taşdemir2Serkan Biliş3Computer Engineering Department, Engineering Faculty, Sinop University, Sinop 57000, TurkeyComputer Engineering Department, Engineering and Natural Sciences Faculty, Kırıkkale University, Kırıkkale 71450, TurkeyComputer Engineering Department, Technology Faculty, Selçuk University, Konya 42250, TurkeyEye Diseases Department, Batıgoz Medical Group Hospital, Izmir 35200, TurkeyHypertensive retinopathy (HR) is a serious eye disease that can lead to permanent vision loss if not diagnosed early. The conventional diagnostic methods are subjective and time-consuming, so there is a need for an automated and reliable system. In this study, a three-stage method that provides high accuracy in HR diagnosis is proposed. In the first stage, 14 well-known Convolutional Neural Network (CNN) models were evaluated, and the top three models were identified. Among these models, DenseNet169 achieved the highest accuracy rate of 87.73%. In the second stage, the deep features obtained from these three models were combined and classified using machine learning (ML) algorithms including Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The SVM with a sigmoid kernel achieved the best performance (92% accuracy). In the third stage, feature selection was performed using metaheuristic optimization techniques including Genetic Algorithm (GA), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Harris Hawk Optimization (HHO). The HHO algorithm increased the classification accuracy to 94.66%, enhancing the model’s generalization ability and reducing misclassifications. The proposed method provides superior accuracy in the diagnosis of HR at different severity levels compared to single-model CNN approaches. These results demonstrate that the integration of Deep Learning (DL), ML, and optimization techniques holds significant potential in automated HR diagnosis.https://www.mdpi.com/2076-3417/15/12/6485Convolutional Neural Networkeye diseasefeature fusionHarris Hawk Optimizationhypertensive retinopathy |
| spellingShingle | Süleyman Burçin Şüyun Mustafa Yurdakul Şakir Taşdemir Serkan Biliş Triple-Stream Deep Feature Selection with Metaheuristic Optimization and Machine Learning for Multi-Stage Hypertensive Retinopathy Diagnosis Applied Sciences Convolutional Neural Network eye disease feature fusion Harris Hawk Optimization hypertensive retinopathy |
| title | Triple-Stream Deep Feature Selection with Metaheuristic Optimization and Machine Learning for Multi-Stage Hypertensive Retinopathy Diagnosis |
| title_full | Triple-Stream Deep Feature Selection with Metaheuristic Optimization and Machine Learning for Multi-Stage Hypertensive Retinopathy Diagnosis |
| title_fullStr | Triple-Stream Deep Feature Selection with Metaheuristic Optimization and Machine Learning for Multi-Stage Hypertensive Retinopathy Diagnosis |
| title_full_unstemmed | Triple-Stream Deep Feature Selection with Metaheuristic Optimization and Machine Learning for Multi-Stage Hypertensive Retinopathy Diagnosis |
| title_short | Triple-Stream Deep Feature Selection with Metaheuristic Optimization and Machine Learning for Multi-Stage Hypertensive Retinopathy Diagnosis |
| title_sort | triple stream deep feature selection with metaheuristic optimization and machine learning for multi stage hypertensive retinopathy diagnosis |
| topic | Convolutional Neural Network eye disease feature fusion Harris Hawk Optimization hypertensive retinopathy |
| url | https://www.mdpi.com/2076-3417/15/12/6485 |
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