Cargando…
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...
Autores principales: | , , , , , , |
---|---|
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 |
_version_ | 1785063877145264128 |
---|---|
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. |
format | Online Article Text |
id | pubmed-10297410 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT sulaimanadel resrandsvmhybridapproachforacutelymphocyticleukemiaclassificationinbloodsmearimages AT kaurswapandeep resrandsvmhybridapproachforacutelymphocyticleukemiaclassificationinbloodsmearimages AT guptasheifali resrandsvmhybridapproachforacutelymphocyticleukemiaclassificationinbloodsmearimages AT alshahranihani resrandsvmhybridapproachforacutelymphocyticleukemiaclassificationinbloodsmearimages AT reshanmanasalehal resrandsvmhybridapproachforacutelymphocyticleukemiaclassificationinbloodsmearimages AT alyamisultan resrandsvmhybridapproachforacutelymphocyticleukemiaclassificationinbloodsmearimages AT shaikhasadullah resrandsvmhybridapproachforacutelymphocyticleukemiaclassificationinbloodsmearimages |