Recognition of Different Types of Leukocytes Using YOLOv2 and Optimized Bag-of-Features
<p>White blood cells (WBCs) protect human body against different types of infections including fungal, parasitic, viral, and bacterial. The detection of abnormal regions in WBCs is a difficult task. Therefore a method is proposed for the localization of WBCs based on YOLOv2-Nucleus-Cytoplasm,...
محفوظ في:
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2020
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| الموضوعات: | |
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| _version_ | 1864513561259147264 |
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| author | Muhammad Sharif (7039565) |
| author2 | Javaria Amin (14557730) Ayesha Siddiqa (16870098) Habib Ullah Khan (12024579) Muhammad Sheraz Arshad Malik (16870101) Muhammad Almas Anjum (16870104) Seifedine Kadry (8713629) |
| author2_role | author author author author author author |
| author_facet | Muhammad Sharif (7039565) Javaria Amin (14557730) Ayesha Siddiqa (16870098) Habib Ullah Khan (12024579) Muhammad Sheraz Arshad Malik (16870101) Muhammad Almas Anjum (16870104) Seifedine Kadry (8713629) |
| author_role | author |
| dc.creator.none.fl_str_mv | Muhammad Sharif (7039565) Javaria Amin (14557730) Ayesha Siddiqa (16870098) Habib Ullah Khan (12024579) Muhammad Sheraz Arshad Malik (16870101) Muhammad Almas Anjum (16870104) Seifedine Kadry (8713629) |
| dc.date.none.fl_str_mv | 2020-09-03T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2020.3021660 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Recognition_of_Different_Types_of_Leukocytes_Using_YOLOv2_and_Optimized_Bag-of-Features/24016137 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Clinical sciences Engineering Biomedical engineering Information and computing sciences Computer vision and multimedia computation Machine learning Feature extraction Cells (biology) Computational modeling White blood cells Image segmentation Machine learning Cytoplasm Leukocytes DarkNet-19 Recognition YOLOv2 |
| dc.title.none.fl_str_mv | Recognition of Different Types of Leukocytes Using YOLOv2 and Optimized Bag-of-Features |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>White blood cells (WBCs) protect human body against different types of infections including fungal, parasitic, viral, and bacterial. The detection of abnormal regions in WBCs is a difficult task. Therefore a method is proposed for the localization of WBCs based on YOLOv2-Nucleus-Cytoplasm, which contains darkNet-19 as a basenetwork of the YOLOv2 model. In this model features are extracted from LeakyReLU-18 of darkNet-19 and supplied as an input to the YOLOv2 model. The YOLOv2-Nucleus-Cytoplasm model localizes and classifies the WBCs with maximum score labels. It also localize the WBCs into the blast and non-blast cells. After localization, the bag-of-features are extracted and optimized by using particle swarm optimization(PSO). The improved feature vector is fed to classifiers i.e., optimized naïve Bayes (O-NB) & optimized discriminant analysis (O-DA) for WBCs classification. The experiments are performed on LISC, ALL-IDB1, and ALL-IDB2 datasets.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2020.3021660" target="_blank">https://dx.doi.org/10.1109/access.2020.3021660</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_987b77b06a278bdd2df611e6af7c82d5 |
| identifier_str_mv | 10.1109/access.2020.3021660 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24016137 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Recognition of Different Types of Leukocytes Using YOLOv2 and Optimized Bag-of-FeaturesMuhammad Sharif (7039565)Javaria Amin (14557730)Ayesha Siddiqa (16870098)Habib Ullah Khan (12024579)Muhammad Sheraz Arshad Malik (16870101)Muhammad Almas Anjum (16870104)Seifedine Kadry (8713629)Biomedical and clinical sciencesClinical sciencesEngineeringBiomedical engineeringInformation and computing sciencesComputer vision and multimedia computationMachine learningFeature extractionCells (biology)Computational modelingWhite blood cellsImage segmentationMachine learningCytoplasmLeukocytesDarkNet-19RecognitionYOLOv2<p>White blood cells (WBCs) protect human body against different types of infections including fungal, parasitic, viral, and bacterial. The detection of abnormal regions in WBCs is a difficult task. Therefore a method is proposed for the localization of WBCs based on YOLOv2-Nucleus-Cytoplasm, which contains darkNet-19 as a basenetwork of the YOLOv2 model. In this model features are extracted from LeakyReLU-18 of darkNet-19 and supplied as an input to the YOLOv2 model. The YOLOv2-Nucleus-Cytoplasm model localizes and classifies the WBCs with maximum score labels. It also localize the WBCs into the blast and non-blast cells. After localization, the bag-of-features are extracted and optimized by using particle swarm optimization(PSO). The improved feature vector is fed to classifiers i.e., optimized naïve Bayes (O-NB) & optimized discriminant analysis (O-DA) for WBCs classification. The experiments are performed on LISC, ALL-IDB1, and ALL-IDB2 datasets.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2020.3021660" target="_blank">https://dx.doi.org/10.1109/access.2020.3021660</a></p>2020-09-03T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2020.3021660https://figshare.com/articles/journal_contribution/Recognition_of_Different_Types_of_Leukocytes_Using_YOLOv2_and_Optimized_Bag-of-Features/24016137CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240161372020-09-03T00:00:00Z |
| spellingShingle | Recognition of Different Types of Leukocytes Using YOLOv2 and Optimized Bag-of-Features Muhammad Sharif (7039565) Biomedical and clinical sciences Clinical sciences Engineering Biomedical engineering Information and computing sciences Computer vision and multimedia computation Machine learning Feature extraction Cells (biology) Computational modeling White blood cells Image segmentation Machine learning Cytoplasm Leukocytes DarkNet-19 Recognition YOLOv2 |
| status_str | publishedVersion |
| title | Recognition of Different Types of Leukocytes Using YOLOv2 and Optimized Bag-of-Features |
| title_full | Recognition of Different Types of Leukocytes Using YOLOv2 and Optimized Bag-of-Features |
| title_fullStr | Recognition of Different Types of Leukocytes Using YOLOv2 and Optimized Bag-of-Features |
| title_full_unstemmed | Recognition of Different Types of Leukocytes Using YOLOv2 and Optimized Bag-of-Features |
| title_short | Recognition of Different Types of Leukocytes Using YOLOv2 and Optimized Bag-of-Features |
| title_sort | Recognition of Different Types of Leukocytes Using YOLOv2 and Optimized Bag-of-Features |
| topic | Biomedical and clinical sciences Clinical sciences Engineering Biomedical engineering Information and computing sciences Computer vision and multimedia computation Machine learning Feature extraction Cells (biology) Computational modeling White blood cells Image segmentation Machine learning Cytoplasm Leukocytes DarkNet-19 Recognition YOLOv2 |