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ResRandSVM: Hybrid Approach for Acute Lymphocytic Leukemia Classification in Blood Smear Images

Acute Lymphocytic Leukemia is a type of cancer that occurs when abnormal white blood cells are produced in the bone marrow which do not function properly, crowding out healthy cells and weakening the immunity of the body and thus its ability to resist infections. It spreads quickly in children’s bod...

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Autores principales: Sulaiman, Adel, Kaur, Swapandeep, Gupta, Sheifali, Alshahrani, Hani, Reshan, Mana Saleh Al, Alyami, Sultan, Shaikh, Asadullah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10297410/
https://www.ncbi.nlm.nih.gov/pubmed/37371016
http://dx.doi.org/10.3390/diagnostics13122121
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author Sulaiman, Adel
Kaur, Swapandeep
Gupta, Sheifali
Alshahrani, Hani
Reshan, Mana Saleh Al
Alyami, Sultan
Shaikh, Asadullah
author_facet Sulaiman, Adel
Kaur, Swapandeep
Gupta, Sheifali
Alshahrani, Hani
Reshan, Mana Saleh Al
Alyami, Sultan
Shaikh, Asadullah
author_sort Sulaiman, Adel
collection PubMed
description Acute Lymphocytic Leukemia is a type of cancer that occurs when abnormal white blood cells are produced in the bone marrow which do not function properly, crowding out healthy cells and weakening the immunity of the body and thus its ability to resist infections. It spreads quickly in children’s bodies, and if not treated promptly it may lead to death. The manual detection of this disease is a tedious and slow task. Machine learning and deep learning techniques are faster than manual detection and more accurate. In this paper, a deep feature selection-based approach ResRandSVM is proposed for the detection of Acute Lymphocytic Leukemia in blood smear images. The proposed approach uses seven deep-learning models: ResNet152, VGG16, DenseNet121, MobileNetV2, InceptionV3, EfficientNetB0 and ResNet50 for deep feature extraction from blood smear images. After that, three feature selection methods are used to extract valuable and important features: analysis of variance (ANOVA), principal component analysis (PCA), and Random Forest. Then the selected feature map is fed to four different classifiers, Adaboost, Support Vector Machine, Artificial Neural Network and Naïve Bayes models, to classify the images into leukemia and normal images. The model performs best with a combination of ResNet50 as a feature extractor, Random Forest as feature selection and Support Vector Machine as a classifier with an accuracy of 0.900, precision of 0.902, recall of 0.957 and F1-score of 0.929.
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spelling pubmed-102974102023-06-28 ResRandSVM: Hybrid Approach for Acute Lymphocytic Leukemia Classification in Blood Smear Images Sulaiman, Adel Kaur, Swapandeep Gupta, Sheifali Alshahrani, Hani Reshan, Mana Saleh Al Alyami, Sultan Shaikh, Asadullah Diagnostics (Basel) Article Acute Lymphocytic Leukemia is a type of cancer that occurs when abnormal white blood cells are produced in the bone marrow which do not function properly, crowding out healthy cells and weakening the immunity of the body and thus its ability to resist infections. It spreads quickly in children’s bodies, and if not treated promptly it may lead to death. The manual detection of this disease is a tedious and slow task. Machine learning and deep learning techniques are faster than manual detection and more accurate. In this paper, a deep feature selection-based approach ResRandSVM is proposed for the detection of Acute Lymphocytic Leukemia in blood smear images. The proposed approach uses seven deep-learning models: ResNet152, VGG16, DenseNet121, MobileNetV2, InceptionV3, EfficientNetB0 and ResNet50 for deep feature extraction from blood smear images. After that, three feature selection methods are used to extract valuable and important features: analysis of variance (ANOVA), principal component analysis (PCA), and Random Forest. Then the selected feature map is fed to four different classifiers, Adaboost, Support Vector Machine, Artificial Neural Network and Naïve Bayes models, to classify the images into leukemia and normal images. The model performs best with a combination of ResNet50 as a feature extractor, Random Forest as feature selection and Support Vector Machine as a classifier with an accuracy of 0.900, precision of 0.902, recall of 0.957 and F1-score of 0.929. MDPI 2023-06-20 /pmc/articles/PMC10297410/ /pubmed/37371016 http://dx.doi.org/10.3390/diagnostics13122121 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
Sulaiman, Adel
Kaur, Swapandeep
Gupta, Sheifali
Alshahrani, Hani
Reshan, Mana Saleh Al
Alyami, Sultan
Shaikh, Asadullah
ResRandSVM: Hybrid Approach for Acute Lymphocytic Leukemia Classification in Blood Smear Images
title ResRandSVM: Hybrid Approach for Acute Lymphocytic Leukemia Classification in Blood Smear Images
title_full ResRandSVM: Hybrid Approach for Acute Lymphocytic Leukemia Classification in Blood Smear Images
title_fullStr ResRandSVM: Hybrid Approach for Acute Lymphocytic Leukemia Classification in Blood Smear Images
title_full_unstemmed ResRandSVM: Hybrid Approach for Acute Lymphocytic Leukemia Classification in Blood Smear Images
title_short ResRandSVM: Hybrid Approach for Acute Lymphocytic Leukemia Classification in Blood Smear Images
title_sort resrandsvm: hybrid approach for acute lymphocytic leukemia classification in blood smear images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10297410/
https://www.ncbi.nlm.nih.gov/pubmed/37371016
http://dx.doi.org/10.3390/diagnostics13122121
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