White blood cell image analysis for infection detection based on virtual hexagonal trellis (VHT) by using deep learning
Abstract White blood cells (WBCs) are an indispensable constituent of the immune system. Efficient and accurate categorization of WBC is a critical task for disease diagnosis by medical experts. This categorization helps in the correct identification of medical problems. In this research work, WBC c...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-10-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-44352-8 |
| _version_ | 1797559906022195200 |
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| author | Shahid Rashid Mudassar Raza Muhammad Sharif Faisal Azam Seifedine Kadry Jungeun Kim |
| author_facet | Shahid Rashid Mudassar Raza Muhammad Sharif Faisal Azam Seifedine Kadry Jungeun Kim |
| author_sort | Shahid Rashid |
| collection | DOAJ |
| description | Abstract White blood cells (WBCs) are an indispensable constituent of the immune system. Efficient and accurate categorization of WBC is a critical task for disease diagnosis by medical experts. This categorization helps in the correct identification of medical problems. In this research work, WBC classes are categorized with the help of a transform learning model in combination with our proposed virtual hexagonal trellis (VHT) structure feature extraction method. The VHT feature extractor is a kernel-based filter model designed over a square lattice. In the first step, Graft Net CNN model is used to extract features of augmented data set images. Later, the VHT base feature extractor extracts useful features. The CNN-extracted features are passed to ant colony optimization (ACO) module for optimal features acquisition. Extracted features from the VHT base filter and ACO are serially merged to create a single feature vector. The merged features are passed to the support vector machine (SVM) variants for optimal classification. Our strategy yields 99.9% accuracy, which outperforms other existing methods. |
| first_indexed | 2024-03-10T17:52:51Z |
| format | Article |
| id | doaj.art-6b704b72338f48baa958887afa5788f6 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-10T17:52:51Z |
| publishDate | 2023-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-6b704b72338f48baa958887afa5788f62023-11-20T09:18:51ZengNature PortfolioScientific Reports2045-23222023-10-0113111810.1038/s41598-023-44352-8White blood cell image analysis for infection detection based on virtual hexagonal trellis (VHT) by using deep learningShahid Rashid0Mudassar Raza1Muhammad Sharif2Faisal Azam3Seifedine Kadry4Jungeun Kim5Department of Computer Science, COMSATS University IslamabadDepartment of Computer Science, COMSATS University IslamabadDepartment of Computer Science, COMSATS University IslamabadDepartment of Computer Science, COMSATS University IslamabadDepartment of Applied Data Science, Noroff University CollegeDepartment of Software, Kongju National UniversityAbstract White blood cells (WBCs) are an indispensable constituent of the immune system. Efficient and accurate categorization of WBC is a critical task for disease diagnosis by medical experts. This categorization helps in the correct identification of medical problems. In this research work, WBC classes are categorized with the help of a transform learning model in combination with our proposed virtual hexagonal trellis (VHT) structure feature extraction method. The VHT feature extractor is a kernel-based filter model designed over a square lattice. In the first step, Graft Net CNN model is used to extract features of augmented data set images. Later, the VHT base feature extractor extracts useful features. The CNN-extracted features are passed to ant colony optimization (ACO) module for optimal features acquisition. Extracted features from the VHT base filter and ACO are serially merged to create a single feature vector. The merged features are passed to the support vector machine (SVM) variants for optimal classification. Our strategy yields 99.9% accuracy, which outperforms other existing methods.https://doi.org/10.1038/s41598-023-44352-8 |
| spellingShingle | Shahid Rashid Mudassar Raza Muhammad Sharif Faisal Azam Seifedine Kadry Jungeun Kim White blood cell image analysis for infection detection based on virtual hexagonal trellis (VHT) by using deep learning Scientific Reports |
| title | White blood cell image analysis for infection detection based on virtual hexagonal trellis (VHT) by using deep learning |
| title_full | White blood cell image analysis for infection detection based on virtual hexagonal trellis (VHT) by using deep learning |
| title_fullStr | White blood cell image analysis for infection detection based on virtual hexagonal trellis (VHT) by using deep learning |
| title_full_unstemmed | White blood cell image analysis for infection detection based on virtual hexagonal trellis (VHT) by using deep learning |
| title_short | White blood cell image analysis for infection detection based on virtual hexagonal trellis (VHT) by using deep learning |
| title_sort | white blood cell image analysis for infection detection based on virtual hexagonal trellis vht by using deep learning |
| url | https://doi.org/10.1038/s41598-023-44352-8 |
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