A New Quantum Circuits of Quantum Convolutional Neural Network for X-Ray Images Classification
A common model for classifying images is the convolutional neural network (CNN), which has the benefit of effectively using data correlation information. Despite their remarkable success, classical CNNs may face challenges in achieving further improvements in accuracy, computational efficiency, expl...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10517587/ |
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| author | Mohammed Yousif Belal Al-Khateeb Begonya Garcia-Zapirain |
| author_facet | Mohammed Yousif Belal Al-Khateeb Begonya Garcia-Zapirain |
| author_sort | Mohammed Yousif |
| collection | DOAJ |
| description | A common model for classifying images is the convolutional neural network (CNN), which has the benefit of effectively using data correlation information. Despite their remarkable success, classical CNNs may face challenges in achieving further improvements in accuracy, computational efficiency, explainability, and generalization. However, if the specified data dimension or model grows too large, CNN becomes difficult to train effectively with a slowdown processing. In order to address a problem using CNN utilizing quantum computing, Quantum Convolutional Neural Network (QCNN) proposes a novel quantum solution or enhances the functionality of an existing learning model in terms of processing time during training. This paper presents a comparative analysis between classical Convolutional Neural Networks (CNNs) and a novel quantum circuit architecture tailored for image-based tasks, emphasizing the adaptability and versatility of quantum circuits in enhancing feature extraction capabilities and then final accuracy and processing time. A MNIST and covidx-cxr3 datasets was used to train quantum-CNN models, and the results of these comparisons were made with traditional CNN performance. The results demonstrate that the suggested QCNN beat the traditional CNN in terms of recognition accuracy and processing speed (process time) when combined with cutting-edge feature extraction techniques. This superiority is particularly evident when trained on the covidx-cxr3 dataset, highlighting the potential for quantum computing to revolutionize image classification tasks. |
| format | Article |
| id | doaj-art-32e6fe91addd49bba566a2968fd319c8 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-32e6fe91addd49bba566a2968fd319c82025-08-20T03:10:07ZengIEEEIEEE Access2169-35362024-01-0112656606567110.1109/ACCESS.2024.339641110517587A New Quantum Circuits of Quantum Convolutional Neural Network for X-Ray Images ClassificationMohammed Yousif0https://orcid.org/0000-0003-1823-297XBelal Al-Khateeb1https://orcid.org/0000-0003-3066-0790Begonya Garcia-Zapirain2https://orcid.org/0000-0002-9356-1186College of Computer Science and Information Technology, University of Anbar, Ramadi, Anbar, IraqCollege of Computer Science and Information Technology, University of Anbar, Ramadi, Anbar, IraqeVIDA Laboratory, University of Deusto, Bilbao, SpainA common model for classifying images is the convolutional neural network (CNN), which has the benefit of effectively using data correlation information. Despite their remarkable success, classical CNNs may face challenges in achieving further improvements in accuracy, computational efficiency, explainability, and generalization. However, if the specified data dimension or model grows too large, CNN becomes difficult to train effectively with a slowdown processing. In order to address a problem using CNN utilizing quantum computing, Quantum Convolutional Neural Network (QCNN) proposes a novel quantum solution or enhances the functionality of an existing learning model in terms of processing time during training. This paper presents a comparative analysis between classical Convolutional Neural Networks (CNNs) and a novel quantum circuit architecture tailored for image-based tasks, emphasizing the adaptability and versatility of quantum circuits in enhancing feature extraction capabilities and then final accuracy and processing time. A MNIST and covidx-cxr3 datasets was used to train quantum-CNN models, and the results of these comparisons were made with traditional CNN performance. The results demonstrate that the suggested QCNN beat the traditional CNN in terms of recognition accuracy and processing speed (process time) when combined with cutting-edge feature extraction techniques. This superiority is particularly evident when trained on the covidx-cxr3 dataset, highlighting the potential for quantum computing to revolutionize image classification tasks.https://ieeexplore.ieee.org/document/10517587/Quantum computingquantum circuitconvolutional neural networkcovid19quantum convolutionquantum pooling |
| spellingShingle | Mohammed Yousif Belal Al-Khateeb Begonya Garcia-Zapirain A New Quantum Circuits of Quantum Convolutional Neural Network for X-Ray Images Classification IEEE Access Quantum computing quantum circuit convolutional neural network covid19 quantum convolution quantum pooling |
| title | A New Quantum Circuits of Quantum Convolutional Neural Network for X-Ray Images Classification |
| title_full | A New Quantum Circuits of Quantum Convolutional Neural Network for X-Ray Images Classification |
| title_fullStr | A New Quantum Circuits of Quantum Convolutional Neural Network for X-Ray Images Classification |
| title_full_unstemmed | A New Quantum Circuits of Quantum Convolutional Neural Network for X-Ray Images Classification |
| title_short | A New Quantum Circuits of Quantum Convolutional Neural Network for X-Ray Images Classification |
| title_sort | new quantum circuits of quantum convolutional neural network for x ray images classification |
| topic | Quantum computing quantum circuit convolutional neural network covid19 quantum convolution quantum pooling |
| url | https://ieeexplore.ieee.org/document/10517587/ |
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