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Adapted Deep Ensemble Learning-Based Voting Classifier for Osteosarcoma Cancer Classification
The study utilizes osteosarcoma hematoxylin and the Eosin-stained image dataset, which is unevenly dispersed, and it raises concerns about the potential impact on the overall performance and reliability of any analyses or models derived from the dataset. In this study, a deep-learning-based convolut...
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/PMC10572954/ https://www.ncbi.nlm.nih.gov/pubmed/37835898 http://dx.doi.org/10.3390/diagnostics13193155 |
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author | Walid, Md. Abul Ala Mollick, Swarnali Shill, Pintu Chandra Baowaly, Mrinal Kanti Islam, Md. Rabiul Ahamad, Md. Martuza Othman, Manal A. Samad, Md Abdus |
author_facet | Walid, Md. Abul Ala Mollick, Swarnali Shill, Pintu Chandra Baowaly, Mrinal Kanti Islam, Md. Rabiul Ahamad, Md. Martuza Othman, Manal A. Samad, Md Abdus |
author_sort | Walid, Md. Abul Ala |
collection | PubMed |
description | The study utilizes osteosarcoma hematoxylin and the Eosin-stained image dataset, which is unevenly dispersed, and it raises concerns about the potential impact on the overall performance and reliability of any analyses or models derived from the dataset. In this study, a deep-learning-based convolution neural network (CNN) and adapted heterogeneous ensemble-learning-based voting classifier have been proposed to classify osteosarcoma. The proposed methods can also resolve the issue and develop unbiased learning models by introducing an evenly distributed training dataset. Data augmentation is employed to boost the generalization abilities. Six different pre-trained CNN models, namely MobileNetV1, Mo-bileNetV2, ResNetV250, InceptionV2, EfficientNetV2B0, and NasNetMobile, are applied and evaluated in frozen and fine-tuned-based phases. In addition, a novel CNN model and adapted heterogeneous ensemble-learning-based voting classifier developed from the proposed CNN model, fine-tuned NasNetMobile model, and fine-tuned Efficient-NetV2B0 model are also introduced to classify osteosarcoma. The proposed CNN model outperforms other pre-trained models. The Kappa score obtained from the proposed CNN model is 93.09%. Notably, the proposed voting classifier attains the highest Kappa score of 96.50% and outperforms all other models. The findings of this study have practical implications in telemedicine, mobile healthcare systems, and as a supportive tool for medical professionals. |
format | Online Article Text |
id | pubmed-10572954 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-105729542023-10-14 Adapted Deep Ensemble Learning-Based Voting Classifier for Osteosarcoma Cancer Classification Walid, Md. Abul Ala Mollick, Swarnali Shill, Pintu Chandra Baowaly, Mrinal Kanti Islam, Md. Rabiul Ahamad, Md. Martuza Othman, Manal A. Samad, Md Abdus Diagnostics (Basel) Article The study utilizes osteosarcoma hematoxylin and the Eosin-stained image dataset, which is unevenly dispersed, and it raises concerns about the potential impact on the overall performance and reliability of any analyses or models derived from the dataset. In this study, a deep-learning-based convolution neural network (CNN) and adapted heterogeneous ensemble-learning-based voting classifier have been proposed to classify osteosarcoma. The proposed methods can also resolve the issue and develop unbiased learning models by introducing an evenly distributed training dataset. Data augmentation is employed to boost the generalization abilities. Six different pre-trained CNN models, namely MobileNetV1, Mo-bileNetV2, ResNetV250, InceptionV2, EfficientNetV2B0, and NasNetMobile, are applied and evaluated in frozen and fine-tuned-based phases. In addition, a novel CNN model and adapted heterogeneous ensemble-learning-based voting classifier developed from the proposed CNN model, fine-tuned NasNetMobile model, and fine-tuned Efficient-NetV2B0 model are also introduced to classify osteosarcoma. The proposed CNN model outperforms other pre-trained models. The Kappa score obtained from the proposed CNN model is 93.09%. Notably, the proposed voting classifier attains the highest Kappa score of 96.50% and outperforms all other models. The findings of this study have practical implications in telemedicine, mobile healthcare systems, and as a supportive tool for medical professionals. MDPI 2023-10-09 /pmc/articles/PMC10572954/ /pubmed/37835898 http://dx.doi.org/10.3390/diagnostics13193155 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 Walid, Md. Abul Ala Mollick, Swarnali Shill, Pintu Chandra Baowaly, Mrinal Kanti Islam, Md. Rabiul Ahamad, Md. Martuza Othman, Manal A. Samad, Md Abdus Adapted Deep Ensemble Learning-Based Voting Classifier for Osteosarcoma Cancer Classification |
title | Adapted Deep Ensemble Learning-Based Voting Classifier for Osteosarcoma Cancer Classification |
title_full | Adapted Deep Ensemble Learning-Based Voting Classifier for Osteosarcoma Cancer Classification |
title_fullStr | Adapted Deep Ensemble Learning-Based Voting Classifier for Osteosarcoma Cancer Classification |
title_full_unstemmed | Adapted Deep Ensemble Learning-Based Voting Classifier for Osteosarcoma Cancer Classification |
title_short | Adapted Deep Ensemble Learning-Based Voting Classifier for Osteosarcoma Cancer Classification |
title_sort | adapted deep ensemble learning-based voting classifier for osteosarcoma cancer classification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10572954/ https://www.ncbi.nlm.nih.gov/pubmed/37835898 http://dx.doi.org/10.3390/diagnostics13193155 |
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