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DTBV: A Deep Transfer-Based Bone Cancer Diagnosis System Using VGG16 Feature Extraction
Among the many different types of cancer, bone cancer is the most lethal and least prevalent. More cases are reported each year. Early diagnosis of bone cancer is crucial since it helps limit the spread of malignant cells and reduce mortality. The manual method of detection of bone cancer is cumbers...
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/PMC9955441/ https://www.ncbi.nlm.nih.gov/pubmed/36832245 http://dx.doi.org/10.3390/diagnostics13040757 |
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author | Suganeshwari, G. Balakumar, R. Karuppanan, Kalimuthu Prathiba, Sahaya Beni Anbalagan, Sudha Raja, Gunasekaran |
author_facet | Suganeshwari, G. Balakumar, R. Karuppanan, Kalimuthu Prathiba, Sahaya Beni Anbalagan, Sudha Raja, Gunasekaran |
author_sort | Suganeshwari, G. |
collection | PubMed |
description | Among the many different types of cancer, bone cancer is the most lethal and least prevalent. More cases are reported each year. Early diagnosis of bone cancer is crucial since it helps limit the spread of malignant cells and reduce mortality. The manual method of detection of bone cancer is cumbersome and requires specialized knowledge. A deep transfer-based bone cancer diagnosis (DTBV) system using VGG16 feature extraction is proposed to address these issues. The proposed DTBV system uses a transfer learning (TL) approach in which a pre-trained convolutional neural network (CNN) model is used to extract features from the pre-processed input image and a support vector machine (SVM) model is used to train using these features to distinguish between cancerous and healthy bone. The CNN is applied to the image datasets as it provides better image recognition with high accuracy when the layers in neural network feature extraction increase. In the proposed DTBV system, the VGG16 model extracts the features from the input X-ray image. A mutual information statistic that measures the dependency between the different features is then used to select the best features. This is the first time this method has been used for detecting bone cancer. Once selected features are selected, they are fed into the SVM classifier. The SVM model classifies the given testing dataset into malignant and benign categories. A comprehensive performance evaluation has demonstrated that the proposed DTBV system is highly efficient in detecting bone cancer, with an accuracy of 93.9%, which is more accurate than other existing systems. |
format | Online Article Text |
id | pubmed-9955441 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99554412023-02-25 DTBV: A Deep Transfer-Based Bone Cancer Diagnosis System Using VGG16 Feature Extraction Suganeshwari, G. Balakumar, R. Karuppanan, Kalimuthu Prathiba, Sahaya Beni Anbalagan, Sudha Raja, Gunasekaran Diagnostics (Basel) Article Among the many different types of cancer, bone cancer is the most lethal and least prevalent. More cases are reported each year. Early diagnosis of bone cancer is crucial since it helps limit the spread of malignant cells and reduce mortality. The manual method of detection of bone cancer is cumbersome and requires specialized knowledge. A deep transfer-based bone cancer diagnosis (DTBV) system using VGG16 feature extraction is proposed to address these issues. The proposed DTBV system uses a transfer learning (TL) approach in which a pre-trained convolutional neural network (CNN) model is used to extract features from the pre-processed input image and a support vector machine (SVM) model is used to train using these features to distinguish between cancerous and healthy bone. The CNN is applied to the image datasets as it provides better image recognition with high accuracy when the layers in neural network feature extraction increase. In the proposed DTBV system, the VGG16 model extracts the features from the input X-ray image. A mutual information statistic that measures the dependency between the different features is then used to select the best features. This is the first time this method has been used for detecting bone cancer. Once selected features are selected, they are fed into the SVM classifier. The SVM model classifies the given testing dataset into malignant and benign categories. A comprehensive performance evaluation has demonstrated that the proposed DTBV system is highly efficient in detecting bone cancer, with an accuracy of 93.9%, which is more accurate than other existing systems. MDPI 2023-02-16 /pmc/articles/PMC9955441/ /pubmed/36832245 http://dx.doi.org/10.3390/diagnostics13040757 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 Suganeshwari, G. Balakumar, R. Karuppanan, Kalimuthu Prathiba, Sahaya Beni Anbalagan, Sudha Raja, Gunasekaran DTBV: A Deep Transfer-Based Bone Cancer Diagnosis System Using VGG16 Feature Extraction |
title | DTBV: A Deep Transfer-Based Bone Cancer Diagnosis System Using VGG16 Feature Extraction |
title_full | DTBV: A Deep Transfer-Based Bone Cancer Diagnosis System Using VGG16 Feature Extraction |
title_fullStr | DTBV: A Deep Transfer-Based Bone Cancer Diagnosis System Using VGG16 Feature Extraction |
title_full_unstemmed | DTBV: A Deep Transfer-Based Bone Cancer Diagnosis System Using VGG16 Feature Extraction |
title_short | DTBV: A Deep Transfer-Based Bone Cancer Diagnosis System Using VGG16 Feature Extraction |
title_sort | dtbv: a deep transfer-based bone cancer diagnosis system using vgg16 feature extraction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9955441/ https://www.ncbi.nlm.nih.gov/pubmed/36832245 http://dx.doi.org/10.3390/diagnostics13040757 |
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