Quantum chimp-enanced SqueezeNet for precise diabetic retinopathy classification
Abstract Diabetic retinopathy remains a prominent cause of blindness throughout the world, and it is a product of prolonged high blood sugar levels that degrade the retinal blood vessels. This makes early detection of DR critical to stop irreversible blindness from occurring. This paper presents an...
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Nature Portfolio
2025-04-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-97686-w |
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| author | Anas Bilal Muhammad Shafiq Waeal J. Obidallah Yousef A. Alduraywish Alishba Tahir Haixia Long |
| author_facet | Anas Bilal Muhammad Shafiq Waeal J. Obidallah Yousef A. Alduraywish Alishba Tahir Haixia Long |
| author_sort | Anas Bilal |
| collection | DOAJ |
| description | Abstract Diabetic retinopathy remains a prominent cause of blindness throughout the world, and it is a product of prolonged high blood sugar levels that degrade the retinal blood vessels. This makes early detection of DR critical to stop irreversible blindness from occurring. This paper presents an advanced hybrid approach that utilizes the Quantum Chimp Optimization Algorithm (QCOA) integrated with SqueezeNet to enhance the accuracy and efficiency of DR classification significantly. The novel methodology was divided into two main stages: feature extraction and classification. Firstly, SqueezeNet enables efficient feature extraction from segmented fundus images with minimal computational complexity, ensuring that critical retinal features are captured effectively. The classification process, QCOA optimizes the Support Vector Machine (SVM) parameters and performs feature selection. The hybrid system effectively refines the performance of the SVM, consequently increasing the classification accuracies and optimizing the model’s performance. By leveraging QCOA’s capability to tune SVM parameters precisely, the proposed approach achieves remarkable classification accuracy, sensitivity, and specificity rates of 99.80%, 99.90%, and 100%, respectively. Fundamentally, the results display the efficiency and practical utility of the proposed approach, and its implementation in real-world clinical settings is likely to significantly improve the rates of early DR reflection and accurate classification for improved patient outcomes. |
| format | Article |
| id | doaj-art-3b11820326a144f0a5270d4dd1a6df82 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-3b11820326a144f0a5270d4dd1a6df822025-08-20T02:17:57ZengNature PortfolioScientific Reports2045-23222025-04-0115111910.1038/s41598-025-97686-wQuantum chimp-enanced SqueezeNet for precise diabetic retinopathy classificationAnas Bilal0Muhammad Shafiq1Waeal J. Obidallah2Yousef A. Alduraywish3Alishba Tahir4Haixia Long5College of Information Science and Technology, Hainan Normal UniversitySchool of Information Engineering, Qujing Normal UniversityCollege of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU)College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU)Shifa College of Medicine, Shifa Tamere Milat UniversityCollege of Information Science and Technology, Hainan Normal UniversityAbstract Diabetic retinopathy remains a prominent cause of blindness throughout the world, and it is a product of prolonged high blood sugar levels that degrade the retinal blood vessels. This makes early detection of DR critical to stop irreversible blindness from occurring. This paper presents an advanced hybrid approach that utilizes the Quantum Chimp Optimization Algorithm (QCOA) integrated with SqueezeNet to enhance the accuracy and efficiency of DR classification significantly. The novel methodology was divided into two main stages: feature extraction and classification. Firstly, SqueezeNet enables efficient feature extraction from segmented fundus images with minimal computational complexity, ensuring that critical retinal features are captured effectively. The classification process, QCOA optimizes the Support Vector Machine (SVM) parameters and performs feature selection. The hybrid system effectively refines the performance of the SVM, consequently increasing the classification accuracies and optimizing the model’s performance. By leveraging QCOA’s capability to tune SVM parameters precisely, the proposed approach achieves remarkable classification accuracy, sensitivity, and specificity rates of 99.80%, 99.90%, and 100%, respectively. Fundamentally, the results display the efficiency and practical utility of the proposed approach, and its implementation in real-world clinical settings is likely to significantly improve the rates of early DR reflection and accurate classification for improved patient outcomes.https://doi.org/10.1038/s41598-025-97686-wDiabetic retinopathyQuantum computingChimp optimizationSupport vector machineMulti-class classification |
| spellingShingle | Anas Bilal Muhammad Shafiq Waeal J. Obidallah Yousef A. Alduraywish Alishba Tahir Haixia Long Quantum chimp-enanced SqueezeNet for precise diabetic retinopathy classification Scientific Reports Diabetic retinopathy Quantum computing Chimp optimization Support vector machine Multi-class classification |
| title | Quantum chimp-enanced SqueezeNet for precise diabetic retinopathy classification |
| title_full | Quantum chimp-enanced SqueezeNet for precise diabetic retinopathy classification |
| title_fullStr | Quantum chimp-enanced SqueezeNet for precise diabetic retinopathy classification |
| title_full_unstemmed | Quantum chimp-enanced SqueezeNet for precise diabetic retinopathy classification |
| title_short | Quantum chimp-enanced SqueezeNet for precise diabetic retinopathy classification |
| title_sort | quantum chimp enanced squeezenet for precise diabetic retinopathy classification |
| topic | Diabetic retinopathy Quantum computing Chimp optimization Support vector machine Multi-class classification |
| url | https://doi.org/10.1038/s41598-025-97686-w |
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