Automatic malaria disease detection from blood cell images using the variational quantum circuit
Variational quantum circuit (VQC) is a quantum-classical (QC) machine learning approach that accommodates quantum processes on a classical computer. Malaria is a worldwide deadly disease caused by Plasmodium parasites. This research designs an effective VQC-based approach to recognize the existence...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-01-01
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| Series: | Informatics in Medicine Unlocked |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352914821002197 |
| _version_ | 1818987343562932224 |
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| author | Muhammad Minoar Hossain Md Abdur Rahim Ali Newaz Bahar Mohammad Motiur Rahman |
| author_facet | Muhammad Minoar Hossain Md Abdur Rahim Ali Newaz Bahar Mohammad Motiur Rahman |
| author_sort | Muhammad Minoar Hossain |
| collection | DOAJ |
| description | Variational quantum circuit (VQC) is a quantum-classical (QC) machine learning approach that accommodates quantum processes on a classical computer. Malaria is a worldwide deadly disease caused by Plasmodium parasites. This research designs an effective VQC-based approach to recognize the existence of malaria from the Red blood cell (RBC) image through the classification of the optimized feature set that has been extracted from a set of RBC images. Minimum redundancy maximum relevance (mRMR), and Principal component analysis (PCA), are used to optimize the feature set. Comparing to existing classical approaches we have found that mRMR with our input encoding and parameterized circuit of VQC shows satisfactory performance by using a lower number of features and a lower number of parameters. After ascertaining the presence of malaria from VQC we have also introduced a rule-based expert system to detect the types of malaria. The proposed mechanism is mainly designed to evaluate the potency of quantum machine learning (QML) in near-term quantum computers and using the ten-fold cross-validation the scheme gained an overall accuracy, precision, recall, and specificity of 99.06%, 99.08%, 99.05%, and 99.07% respectively for malaria disease diagnosis. |
| first_indexed | 2024-12-20T19:05:11Z |
| format | Article |
| id | doaj.art-fa5d87a8b02948fe9e5d27576d6452a8 |
| institution | Directory Open Access Journal |
| issn | 2352-9148 |
| language | English |
| last_indexed | 2024-12-20T19:05:11Z |
| publishDate | 2021-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Informatics in Medicine Unlocked |
| spelling | doaj.art-fa5d87a8b02948fe9e5d27576d6452a82022-12-21T19:29:17ZengElsevierInformatics in Medicine Unlocked2352-91482021-01-0126100743Automatic malaria disease detection from blood cell images using the variational quantum circuitMuhammad Minoar Hossain0Md Abdur Rahim1Ali Newaz Bahar2Mohammad Motiur Rahman3Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Santosh, Tangail, 1902, Bangladesh; Corresponding author.Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Santosh, Tangail, 1902, BangladeshDepartment of information and communication technology, Mawlana Bhashani Science and Technology University, Santosh, Tangail, 1902, BangladeshDepartment of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Santosh, Tangail, 1902, BangladeshVariational quantum circuit (VQC) is a quantum-classical (QC) machine learning approach that accommodates quantum processes on a classical computer. Malaria is a worldwide deadly disease caused by Plasmodium parasites. This research designs an effective VQC-based approach to recognize the existence of malaria from the Red blood cell (RBC) image through the classification of the optimized feature set that has been extracted from a set of RBC images. Minimum redundancy maximum relevance (mRMR), and Principal component analysis (PCA), are used to optimize the feature set. Comparing to existing classical approaches we have found that mRMR with our input encoding and parameterized circuit of VQC shows satisfactory performance by using a lower number of features and a lower number of parameters. After ascertaining the presence of malaria from VQC we have also introduced a rule-based expert system to detect the types of malaria. The proposed mechanism is mainly designed to evaluate the potency of quantum machine learning (QML) in near-term quantum computers and using the ten-fold cross-validation the scheme gained an overall accuracy, precision, recall, and specificity of 99.06%, 99.08%, 99.05%, and 99.07% respectively for malaria disease diagnosis.http://www.sciencedirect.com/science/article/pii/S2352914821002197Quantum computingVariational quantum circuit (VQC)QubitContourlet transform (CT) |
| spellingShingle | Muhammad Minoar Hossain Md Abdur Rahim Ali Newaz Bahar Mohammad Motiur Rahman Automatic malaria disease detection from blood cell images using the variational quantum circuit Informatics in Medicine Unlocked Quantum computing Variational quantum circuit (VQC) Qubit Contourlet transform (CT) |
| title | Automatic malaria disease detection from blood cell images using the variational quantum circuit |
| title_full | Automatic malaria disease detection from blood cell images using the variational quantum circuit |
| title_fullStr | Automatic malaria disease detection from blood cell images using the variational quantum circuit |
| title_full_unstemmed | Automatic malaria disease detection from blood cell images using the variational quantum circuit |
| title_short | Automatic malaria disease detection from blood cell images using the variational quantum circuit |
| title_sort | automatic malaria disease detection from blood cell images using the variational quantum circuit |
| topic | Quantum computing Variational quantum circuit (VQC) Qubit Contourlet transform (CT) |
| url | http://www.sciencedirect.com/science/article/pii/S2352914821002197 |
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