Quantum Mechanism-Based Convolution Model for the Classification of Pathogenic Bacteria
Water, especially drinking water, should be clean and free of disease-causing bacteria because of its critical role in life. However, it isn’t easy to identify and classify them rapidly at an early stage. Primarily, the examination of water is performed manually to check the contamination...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10339292/ |
| _version_ | 1797289852513812480 |
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| author | Isra Naz Jamal Hussain Shah Muhammad Habib Ur Rehman Muhammad Rafiq Gyu Sang Choi |
| author_facet | Isra Naz Jamal Hussain Shah Muhammad Habib Ur Rehman Muhammad Rafiq Gyu Sang Choi |
| author_sort | Isra Naz |
| collection | DOAJ |
| description | Water, especially drinking water, should be clean and free of disease-causing bacteria because of its critical role in life. However, it isn’t easy to identify and classify them rapidly at an early stage. Primarily, the examination of water is performed manually to check the contamination level. Some researchers have proposed techniques to detect and classify bacteria images, but this field still needs more attention. In this research work, a robust Quantum Convolutional Neural Network (QCNN) classification model is proposed to classify the six major categories of pathogenic bacteria. For the acquisition of pathogen images, different slides are created through the gram-staining process, and then images are captured from those slides. DIBaS is the publicly available dataset that provides these slides captured through gram-staining, which is used to evaluate the proposed methodology. So, in the first step, database preprocessing, small patches are extracted from slide images. However, the extracted patches were not clear and very useful, so the Enhanced Super-Resolution Generative Adversarial Network Model (ESRGAN) was applied to images to improve the image quality of extracted patches. The third step is to extract the deep features and classify bacterial images using the QCNN model, in which the Quantum Convolutional layer is added, and classical data is converted into quantum data to perform classification. Based on the results of classification experiments using the QCNN model, the accuracy is 96.54%. |
| first_indexed | 2024-03-07T19:12:00Z |
| format | Article |
| id | doaj.art-21beeef6bbdf423dae659bf1e054f04e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-07T19:12:00Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-21beeef6bbdf423dae659bf1e054f04e2024-03-01T00:00:19ZengIEEEIEEE Access2169-35362023-01-011113774713775710.1109/ACCESS.2023.333912710339292Quantum Mechanism-Based Convolution Model for the Classification of Pathogenic BacteriaIsra Naz0Jamal Hussain Shah1Muhammad Habib Ur Rehman2https://orcid.org/0000-0001-7428-2272Muhammad Rafiq3https://orcid.org/0000-0001-6713-8766Gyu Sang Choi4https://orcid.org/0000-0001-6713-8766Department of Computer Science, COMSATS University Islamabad, Wah Campus, Islamabad, PakistanDepartment of Computer Science, COMSATS University Islamabad, Wah Campus, Islamabad, PakistanDepartment of Computer Science, COMSATS University Islamabad, Wah Campus, Islamabad, PakistanDepartment of Game Software, Keimyung University, Daegu, Dalseo-gu, Republic of KoreaDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan-si, Republic of KoreaWater, especially drinking water, should be clean and free of disease-causing bacteria because of its critical role in life. However, it isn’t easy to identify and classify them rapidly at an early stage. Primarily, the examination of water is performed manually to check the contamination level. Some researchers have proposed techniques to detect and classify bacteria images, but this field still needs more attention. In this research work, a robust Quantum Convolutional Neural Network (QCNN) classification model is proposed to classify the six major categories of pathogenic bacteria. For the acquisition of pathogen images, different slides are created through the gram-staining process, and then images are captured from those slides. DIBaS is the publicly available dataset that provides these slides captured through gram-staining, which is used to evaluate the proposed methodology. So, in the first step, database preprocessing, small patches are extracted from slide images. However, the extracted patches were not clear and very useful, so the Enhanced Super-Resolution Generative Adversarial Network Model (ESRGAN) was applied to images to improve the image quality of extracted patches. The third step is to extract the deep features and classify bacterial images using the QCNN model, in which the Quantum Convolutional layer is added, and classical data is converted into quantum data to perform classification. Based on the results of classification experiments using the QCNN model, the accuracy is 96.54%.https://ieeexplore.ieee.org/document/10339292/Generative adversarial network (GAN)pathogensquantum convolutional network (QCNN)super resolution |
| spellingShingle | Isra Naz Jamal Hussain Shah Muhammad Habib Ur Rehman Muhammad Rafiq Gyu Sang Choi Quantum Mechanism-Based Convolution Model for the Classification of Pathogenic Bacteria IEEE Access Generative adversarial network (GAN) pathogens quantum convolutional network (QCNN) super resolution |
| title | Quantum Mechanism-Based Convolution Model for the Classification of Pathogenic Bacteria |
| title_full | Quantum Mechanism-Based Convolution Model for the Classification of Pathogenic Bacteria |
| title_fullStr | Quantum Mechanism-Based Convolution Model for the Classification of Pathogenic Bacteria |
| title_full_unstemmed | Quantum Mechanism-Based Convolution Model for the Classification of Pathogenic Bacteria |
| title_short | Quantum Mechanism-Based Convolution Model for the Classification of Pathogenic Bacteria |
| title_sort | quantum mechanism based convolution model for the classification of pathogenic bacteria |
| topic | Generative adversarial network (GAN) pathogens quantum convolutional network (QCNN) super resolution |
| url | https://ieeexplore.ieee.org/document/10339292/ |
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