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A Novel Hybrid Approach for Classifying Osteosarcoma Using Deep Feature Extraction and Multilayer Perceptron

Osteosarcoma is the most common type of bone cancer that tends to occur in teenagers and young adults. Due to crowded context, inter-class similarity, inter-class variation, and noise in H&E-stained (hematoxylin and eosin stain) histology tissue, pathologists frequently face difficulty in osteos...

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Autores principales: Aziz, Md. Tarek, Mahmud, S. M. Hasan, Elahe, Md. Fazla, Jahan, Hosney, Rahman, Md Habibur, Nandi, Dip, Smirani, Lassaad K., Ahmed, Kawsar, Bui, Francis M., Moni, Mohammad Ali
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10297313/
https://www.ncbi.nlm.nih.gov/pubmed/37371001
http://dx.doi.org/10.3390/diagnostics13122106
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author Aziz, Md. Tarek
Mahmud, S. M. Hasan
Elahe, Md. Fazla
Jahan, Hosney
Rahman, Md Habibur
Nandi, Dip
Smirani, Lassaad K.
Ahmed, Kawsar
Bui, Francis M.
Moni, Mohammad Ali
author_facet Aziz, Md. Tarek
Mahmud, S. M. Hasan
Elahe, Md. Fazla
Jahan, Hosney
Rahman, Md Habibur
Nandi, Dip
Smirani, Lassaad K.
Ahmed, Kawsar
Bui, Francis M.
Moni, Mohammad Ali
author_sort Aziz, Md. Tarek
collection PubMed
description Osteosarcoma is the most common type of bone cancer that tends to occur in teenagers and young adults. Due to crowded context, inter-class similarity, inter-class variation, and noise in H&E-stained (hematoxylin and eosin stain) histology tissue, pathologists frequently face difficulty in osteosarcoma tumor classification. In this paper, we introduced a hybrid framework for improving the efficiency of three types of osteosarcoma tumor (nontumor, necrosis, and viable tumor) classification by merging different types of CNN-based architectures with a multilayer perceptron (MLP) algorithm on the WSI (whole slide images) dataset. We performed various kinds of preprocessing on the WSI images. Then, five pre-trained CNN models were trained with multiple parameter settings to extract insightful features via transfer learning, where convolution combined with pooling was utilized as a feature extractor. For feature selection, a decision tree-based RFE was designed to recursively eliminate less significant features to improve the model generalization performance for accurate prediction. Here, a decision tree was used as an estimator to select the different features. Finally, a modified MLP classifier was employed to classify binary and multiclass types of osteosarcoma under the five-fold CV to assess the robustness of our proposed hybrid model. Moreover, the feature selection criteria were analyzed to select the optimal one based on their execution time and accuracy. The proposed model achieved an accuracy of 95.2% for multiclass classification and 99.4% for binary classification. Experimental findings indicate that our proposed model significantly outperforms existing methods; therefore, this model could be applicable to support doctors in osteosarcoma diagnosis in clinics. In addition, our proposed model is integrated into a web application using the FastAPI web framework to provide a real-time prediction.
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spelling pubmed-102973132023-06-28 A Novel Hybrid Approach for Classifying Osteosarcoma Using Deep Feature Extraction and Multilayer Perceptron Aziz, Md. Tarek Mahmud, S. M. Hasan Elahe, Md. Fazla Jahan, Hosney Rahman, Md Habibur Nandi, Dip Smirani, Lassaad K. Ahmed, Kawsar Bui, Francis M. Moni, Mohammad Ali Diagnostics (Basel) Article Osteosarcoma is the most common type of bone cancer that tends to occur in teenagers and young adults. Due to crowded context, inter-class similarity, inter-class variation, and noise in H&E-stained (hematoxylin and eosin stain) histology tissue, pathologists frequently face difficulty in osteosarcoma tumor classification. In this paper, we introduced a hybrid framework for improving the efficiency of three types of osteosarcoma tumor (nontumor, necrosis, and viable tumor) classification by merging different types of CNN-based architectures with a multilayer perceptron (MLP) algorithm on the WSI (whole slide images) dataset. We performed various kinds of preprocessing on the WSI images. Then, five pre-trained CNN models were trained with multiple parameter settings to extract insightful features via transfer learning, where convolution combined with pooling was utilized as a feature extractor. For feature selection, a decision tree-based RFE was designed to recursively eliminate less significant features to improve the model generalization performance for accurate prediction. Here, a decision tree was used as an estimator to select the different features. Finally, a modified MLP classifier was employed to classify binary and multiclass types of osteosarcoma under the five-fold CV to assess the robustness of our proposed hybrid model. Moreover, the feature selection criteria were analyzed to select the optimal one based on their execution time and accuracy. The proposed model achieved an accuracy of 95.2% for multiclass classification and 99.4% for binary classification. Experimental findings indicate that our proposed model significantly outperforms existing methods; therefore, this model could be applicable to support doctors in osteosarcoma diagnosis in clinics. In addition, our proposed model is integrated into a web application using the FastAPI web framework to provide a real-time prediction. MDPI 2023-06-18 /pmc/articles/PMC10297313/ /pubmed/37371001 http://dx.doi.org/10.3390/diagnostics13122106 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
Aziz, Md. Tarek
Mahmud, S. M. Hasan
Elahe, Md. Fazla
Jahan, Hosney
Rahman, Md Habibur
Nandi, Dip
Smirani, Lassaad K.
Ahmed, Kawsar
Bui, Francis M.
Moni, Mohammad Ali
A Novel Hybrid Approach for Classifying Osteosarcoma Using Deep Feature Extraction and Multilayer Perceptron
title A Novel Hybrid Approach for Classifying Osteosarcoma Using Deep Feature Extraction and Multilayer Perceptron
title_full A Novel Hybrid Approach for Classifying Osteosarcoma Using Deep Feature Extraction and Multilayer Perceptron
title_fullStr A Novel Hybrid Approach for Classifying Osteosarcoma Using Deep Feature Extraction and Multilayer Perceptron
title_full_unstemmed A Novel Hybrid Approach for Classifying Osteosarcoma Using Deep Feature Extraction and Multilayer Perceptron
title_short A Novel Hybrid Approach for Classifying Osteosarcoma Using Deep Feature Extraction and Multilayer Perceptron
title_sort novel hybrid approach for classifying osteosarcoma using deep feature extraction and multilayer perceptron
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10297313/
https://www.ncbi.nlm.nih.gov/pubmed/37371001
http://dx.doi.org/10.3390/diagnostics13122106
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