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White Blood Cells Classification Using Entropy-Controlled Deep Features Optimization
White blood cells (WBCs) constitute an essential part of the human immune system. The correct identification of WBC subtypes is critical in the diagnosis of leukemia, a kind of blood cancer defined by the aberrant proliferation of malignant leukocytes in the bone marrow. The traditional approach of...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9914384/ https://www.ncbi.nlm.nih.gov/pubmed/36766457 http://dx.doi.org/10.3390/diagnostics13030352 |
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author | Ahmad, Riaz Awais, Muhammad Kausar, Nabeela Akram, Tallha |
author_facet | Ahmad, Riaz Awais, Muhammad Kausar, Nabeela Akram, Tallha |
author_sort | Ahmad, Riaz |
collection | PubMed |
description | White blood cells (WBCs) constitute an essential part of the human immune system. The correct identification of WBC subtypes is critical in the diagnosis of leukemia, a kind of blood cancer defined by the aberrant proliferation of malignant leukocytes in the bone marrow. The traditional approach of classifying WBCs, which involves the visual analysis of blood smear images, is labor-intensive and error-prone. Modern approaches based on deep convolutional neural networks provide significant results for this type of image categorization, but have high processing and implementation costs owing to very large feature sets. This paper presents an improved hybrid approach for efficient WBC subtype classification. First, optimum deep features are extracted from enhanced and segmented WBC images using transfer learning on pre-trained deep neural networks, i.e., DenseNet201 and Darknet53. The serially fused feature vector is then filtered using an entropy-controlled marine predator algorithm (ECMPA). This nature-inspired meta-heuristic optimization algorithm selects the most dominant features while discarding the weak ones. The reduced feature vector is classified with multiple baseline classifiers with various kernel settings. The proposed methodology is validated on a public dataset of 5000 synthetic images that correspond to five different subtypes of WBCs. The system achieves an overall average accuracy of [Formula: see text] with more than [Formula: see text] reduction in the size of the feature vector. The feature selection algorithm also demonstrates better convergence performance as compared to classical meta-heuristic algorithms. The proposed method also demonstrates a comparable performance with several existing works on WBC classification. |
format | Online Article Text |
id | pubmed-9914384 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99143842023-02-11 White Blood Cells Classification Using Entropy-Controlled Deep Features Optimization Ahmad, Riaz Awais, Muhammad Kausar, Nabeela Akram, Tallha Diagnostics (Basel) Article White blood cells (WBCs) constitute an essential part of the human immune system. The correct identification of WBC subtypes is critical in the diagnosis of leukemia, a kind of blood cancer defined by the aberrant proliferation of malignant leukocytes in the bone marrow. The traditional approach of classifying WBCs, which involves the visual analysis of blood smear images, is labor-intensive and error-prone. Modern approaches based on deep convolutional neural networks provide significant results for this type of image categorization, but have high processing and implementation costs owing to very large feature sets. This paper presents an improved hybrid approach for efficient WBC subtype classification. First, optimum deep features are extracted from enhanced and segmented WBC images using transfer learning on pre-trained deep neural networks, i.e., DenseNet201 and Darknet53. The serially fused feature vector is then filtered using an entropy-controlled marine predator algorithm (ECMPA). This nature-inspired meta-heuristic optimization algorithm selects the most dominant features while discarding the weak ones. The reduced feature vector is classified with multiple baseline classifiers with various kernel settings. The proposed methodology is validated on a public dataset of 5000 synthetic images that correspond to five different subtypes of WBCs. The system achieves an overall average accuracy of [Formula: see text] with more than [Formula: see text] reduction in the size of the feature vector. The feature selection algorithm also demonstrates better convergence performance as compared to classical meta-heuristic algorithms. The proposed method also demonstrates a comparable performance with several existing works on WBC classification. MDPI 2023-01-18 /pmc/articles/PMC9914384/ /pubmed/36766457 http://dx.doi.org/10.3390/diagnostics13030352 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ahmad, Riaz Awais, Muhammad Kausar, Nabeela Akram, Tallha White Blood Cells Classification Using Entropy-Controlled Deep Features Optimization |
title | White Blood Cells Classification Using Entropy-Controlled Deep Features Optimization |
title_full | White Blood Cells Classification Using Entropy-Controlled Deep Features Optimization |
title_fullStr | White Blood Cells Classification Using Entropy-Controlled Deep Features Optimization |
title_full_unstemmed | White Blood Cells Classification Using Entropy-Controlled Deep Features Optimization |
title_short | White Blood Cells Classification Using Entropy-Controlled Deep Features Optimization |
title_sort | white blood cells classification using entropy-controlled deep features optimization |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9914384/ https://www.ncbi.nlm.nih.gov/pubmed/36766457 http://dx.doi.org/10.3390/diagnostics13030352 |
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