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Using deep DenseNet with cyclical learning rate to classify leukocytes for leukemia identification
BACKGROUND: The examination, counting, and classification of white blood cells (WBCs), also known as leukocytes, are essential processes in the diagnosis of many disorders, including leukemia, a kind of blood cancer characterized by the uncontrolled proliferation of carcinogenic leukocytes in the ma...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10523295/ https://www.ncbi.nlm.nih.gov/pubmed/37771437 http://dx.doi.org/10.3389/fonc.2023.1230434 |
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author | Houssein, Essam H. Mohamed, Osama Abdel Samee, Nagwan Mahmoud, Noha F. Talaat, Rawan Al-Hejri, Aymen M. Al-Tam, Riyadh M. |
author_facet | Houssein, Essam H. Mohamed, Osama Abdel Samee, Nagwan Mahmoud, Noha F. Talaat, Rawan Al-Hejri, Aymen M. Al-Tam, Riyadh M. |
author_sort | Houssein, Essam H. |
collection | PubMed |
description | BACKGROUND: The examination, counting, and classification of white blood cells (WBCs), also known as leukocytes, are essential processes in the diagnosis of many disorders, including leukemia, a kind of blood cancer characterized by the uncontrolled proliferation of carcinogenic leukocytes in the marrow of the bone. Blood smears can be chemically or microscopically studied to better understand hematological diseases and blood disorders. Detecting, identifying, and categorizing the many blood cell types are essential for disease diagnosis and therapy planning. A theoretical and practical issue. However, methods based on deep learning (DL) have greatly helped blood cell classification. MATERIALS AND METHODS: Images of blood cells in a microscopic smear were collected from GitHub, a public source that uses the MIT license. An end-to-end computer-aided diagnosis (CAD) system for leukocytes has been created and implemented as part of this study. The introduced system comprises image preprocessing and enhancement, image segmentation, feature extraction and selection, and WBC classification. By combining the DenseNet-161 and the cyclical learning rate (CLR), we contribute an approach that speeds up hyperparameter optimization. We also offer the one-cycle technique to rapidly optimize all hyperparameters of DL models to boost training performance. RESULTS: The dataset has been split into two sets: approximately 80% of the data (9,966 images) for the training set and 20% (2,487 images) for the validation set. The validation set has 623, 620, 620, and 624 eosinophil, lymphocyte, monocyte, and neutrophil images, whereas the training set has 2,497, 2,483, 2,487, and 2,499, respectively. The suggested method has 100% accuracy on the training set of images and 99.8% accuracy on the testing set. CONCLUSION: Using a combination of the recently developed pretrained convolutional neural network (CNN), DenseNet, and the one fit cycle policy, this study describes a technique of training for the classification of WBCs for leukemia detection. The proposed method is more accurate compared to the state of the art. |
format | Online Article Text |
id | pubmed-10523295 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105232952023-09-28 Using deep DenseNet with cyclical learning rate to classify leukocytes for leukemia identification Houssein, Essam H. Mohamed, Osama Abdel Samee, Nagwan Mahmoud, Noha F. Talaat, Rawan Al-Hejri, Aymen M. Al-Tam, Riyadh M. Front Oncol Oncology BACKGROUND: The examination, counting, and classification of white blood cells (WBCs), also known as leukocytes, are essential processes in the diagnosis of many disorders, including leukemia, a kind of blood cancer characterized by the uncontrolled proliferation of carcinogenic leukocytes in the marrow of the bone. Blood smears can be chemically or microscopically studied to better understand hematological diseases and blood disorders. Detecting, identifying, and categorizing the many blood cell types are essential for disease diagnosis and therapy planning. A theoretical and practical issue. However, methods based on deep learning (DL) have greatly helped blood cell classification. MATERIALS AND METHODS: Images of blood cells in a microscopic smear were collected from GitHub, a public source that uses the MIT license. An end-to-end computer-aided diagnosis (CAD) system for leukocytes has been created and implemented as part of this study. The introduced system comprises image preprocessing and enhancement, image segmentation, feature extraction and selection, and WBC classification. By combining the DenseNet-161 and the cyclical learning rate (CLR), we contribute an approach that speeds up hyperparameter optimization. We also offer the one-cycle technique to rapidly optimize all hyperparameters of DL models to boost training performance. RESULTS: The dataset has been split into two sets: approximately 80% of the data (9,966 images) for the training set and 20% (2,487 images) for the validation set. The validation set has 623, 620, 620, and 624 eosinophil, lymphocyte, monocyte, and neutrophil images, whereas the training set has 2,497, 2,483, 2,487, and 2,499, respectively. The suggested method has 100% accuracy on the training set of images and 99.8% accuracy on the testing set. CONCLUSION: Using a combination of the recently developed pretrained convolutional neural network (CNN), DenseNet, and the one fit cycle policy, this study describes a technique of training for the classification of WBCs for leukemia detection. The proposed method is more accurate compared to the state of the art. Frontiers Media S.A. 2023-09-12 /pmc/articles/PMC10523295/ /pubmed/37771437 http://dx.doi.org/10.3389/fonc.2023.1230434 Text en Copyright © 2023 Houssein, Mohamed, Abdel Samee, Mahmoud, Talaat, Al-Hejri and Al-Tam https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Houssein, Essam H. Mohamed, Osama Abdel Samee, Nagwan Mahmoud, Noha F. Talaat, Rawan Al-Hejri, Aymen M. Al-Tam, Riyadh M. Using deep DenseNet with cyclical learning rate to classify leukocytes for leukemia identification |
title | Using deep DenseNet with cyclical learning rate to classify leukocytes for leukemia identification |
title_full | Using deep DenseNet with cyclical learning rate to classify leukocytes for leukemia identification |
title_fullStr | Using deep DenseNet with cyclical learning rate to classify leukocytes for leukemia identification |
title_full_unstemmed | Using deep DenseNet with cyclical learning rate to classify leukocytes for leukemia identification |
title_short | Using deep DenseNet with cyclical learning rate to classify leukocytes for leukemia identification |
title_sort | using deep densenet with cyclical learning rate to classify leukocytes for leukemia identification |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10523295/ https://www.ncbi.nlm.nih.gov/pubmed/37771437 http://dx.doi.org/10.3389/fonc.2023.1230434 |
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