Quantum Computing Meets Deep Learning: A Promising Approach for Diabetic Retinopathy Classification
Diabetic retinopathy seems to be the cause of micro-vascular retinal alterations. It remains a leading reason for blindness and vision loss in adults around the age of 20 to 74. Screening for this disease has become vital in identifying referable cases that require complete ophthalmic evaluation and...
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MDPI AG
2023-04-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/9/2008 |
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| author | Shtwai Alsubai Abdullah Alqahtani Adel Binbusayyis Mohemmed Sha Abdu Gumaei Shuihua Wang |
| author_facet | Shtwai Alsubai Abdullah Alqahtani Adel Binbusayyis Mohemmed Sha Abdu Gumaei Shuihua Wang |
| author_sort | Shtwai Alsubai |
| collection | DOAJ |
| description | Diabetic retinopathy seems to be the cause of micro-vascular retinal alterations. It remains a leading reason for blindness and vision loss in adults around the age of 20 to 74. Screening for this disease has become vital in identifying referable cases that require complete ophthalmic evaluation and treatment to avoid permanent loss of vision. The computer-aided design could ease this screening process, which requires limited time, and assist clinicians. The main complexity in classifying images involves huge computation, leading to slow classification. Certain image classification approaches integrating quantum computing have recently evolved to resolve this. With its parallel computing ability, quantum computing could assist in effective classification. The notion of integrating quantum computing with conventional image classification methods is theoretically feasible and advantageous. However, as existing image classification techniques have failed to procure high accuracy in classification, a robust approach is needed. The present research proposes a quantum-based deep convolutional neural network to avert these pitfalls and identify disease grades from the Indian Diabetic Retinopathy Image Dataset. Typically, quantum computing could make use of the maximum number of entangled qubits for image reconstruction without any additional information. This study involves conceptual enhancement by proposing an optimized structural system termed an optimized multiple-qbit gate quantum neural network for the classification of DR. In this case, multiple qubits are regarded as the ability of qubits in multiple states to exist concurrently, which permits performance improvement with the distinct additional qubit. The overall performance of this system is validated in accordance with performance metrics, and the proposed method achieves 100% accuracy, 100% precision, 100% recall, 100% specificity, and 100% f1-score. |
| first_indexed | 2024-03-11T04:13:02Z |
| format | Article |
| id | doaj.art-3a1c08a94ffe45e9a2db6721b8f5cb7c |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T04:13:02Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-3a1c08a94ffe45e9a2db6721b8f5cb7c2023-11-17T23:18:48ZengMDPI AGMathematics2227-73902023-04-01119200810.3390/math11092008Quantum Computing Meets Deep Learning: A Promising Approach for Diabetic Retinopathy ClassificationShtwai Alsubai0Abdullah Alqahtani1Adel Binbusayyis2Mohemmed Sha3Abdu Gumaei4Shuihua Wang5Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi ArabiaDepartment of Software Engineering, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi ArabiaDepartment of Software Engineering, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi ArabiaDepartment of Software Engineering, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi ArabiaDepartment of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi ArabiaDepartment of Mathematics, University of Leicester, Leicester LE1 7RH, UKDiabetic retinopathy seems to be the cause of micro-vascular retinal alterations. It remains a leading reason for blindness and vision loss in adults around the age of 20 to 74. Screening for this disease has become vital in identifying referable cases that require complete ophthalmic evaluation and treatment to avoid permanent loss of vision. The computer-aided design could ease this screening process, which requires limited time, and assist clinicians. The main complexity in classifying images involves huge computation, leading to slow classification. Certain image classification approaches integrating quantum computing have recently evolved to resolve this. With its parallel computing ability, quantum computing could assist in effective classification. The notion of integrating quantum computing with conventional image classification methods is theoretically feasible and advantageous. However, as existing image classification techniques have failed to procure high accuracy in classification, a robust approach is needed. The present research proposes a quantum-based deep convolutional neural network to avert these pitfalls and identify disease grades from the Indian Diabetic Retinopathy Image Dataset. Typically, quantum computing could make use of the maximum number of entangled qubits for image reconstruction without any additional information. This study involves conceptual enhancement by proposing an optimized structural system termed an optimized multiple-qbit gate quantum neural network for the classification of DR. In this case, multiple qubits are regarded as the ability of qubits in multiple states to exist concurrently, which permits performance improvement with the distinct additional qubit. The overall performance of this system is validated in accordance with performance metrics, and the proposed method achieves 100% accuracy, 100% precision, 100% recall, 100% specificity, and 100% f1-score.https://www.mdpi.com/2227-7390/11/9/2008diabetic retinopathydeep convolutional neural networkquantum-based neural networkHadamard gatecoupling gatemultiple qubits |
| spellingShingle | Shtwai Alsubai Abdullah Alqahtani Adel Binbusayyis Mohemmed Sha Abdu Gumaei Shuihua Wang Quantum Computing Meets Deep Learning: A Promising Approach for Diabetic Retinopathy Classification Mathematics diabetic retinopathy deep convolutional neural network quantum-based neural network Hadamard gate coupling gate multiple qubits |
| title | Quantum Computing Meets Deep Learning: A Promising Approach for Diabetic Retinopathy Classification |
| title_full | Quantum Computing Meets Deep Learning: A Promising Approach for Diabetic Retinopathy Classification |
| title_fullStr | Quantum Computing Meets Deep Learning: A Promising Approach for Diabetic Retinopathy Classification |
| title_full_unstemmed | Quantum Computing Meets Deep Learning: A Promising Approach for Diabetic Retinopathy Classification |
| title_short | Quantum Computing Meets Deep Learning: A Promising Approach for Diabetic Retinopathy Classification |
| title_sort | quantum computing meets deep learning a promising approach for diabetic retinopathy classification |
| topic | diabetic retinopathy deep convolutional neural network quantum-based neural network Hadamard gate coupling gate multiple qubits |
| url | https://www.mdpi.com/2227-7390/11/9/2008 |
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