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Diagnosis of Oral Squamous Cell Carcinoma Using Deep Neural Networks and Binary Particle Swarm Optimization on Histopathological Images: An AIoMT Approach

Overall prediction of oral cavity squamous cell carcinoma (OCSCC) remains inadequate, as more than half of patients with oral cavity cancer are detected at later stages. It is generally accepted that the differential diagnosis of OCSCC is usually difficult and requires expertise and experience. Diag...

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Autores principales: Deif, Mohanad A., Attar, Hani, Amer, Ayman, Elhaty, Ismail A., Khosravi, Mohammad R., Solyman, Ahmed A. A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9546660/
https://www.ncbi.nlm.nih.gov/pubmed/36210968
http://dx.doi.org/10.1155/2022/6364102
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author Deif, Mohanad A.
Attar, Hani
Amer, Ayman
Elhaty, Ismail A.
Khosravi, Mohammad R.
Solyman, Ahmed A. A.
author_facet Deif, Mohanad A.
Attar, Hani
Amer, Ayman
Elhaty, Ismail A.
Khosravi, Mohammad R.
Solyman, Ahmed A. A.
author_sort Deif, Mohanad A.
collection PubMed
description Overall prediction of oral cavity squamous cell carcinoma (OCSCC) remains inadequate, as more than half of patients with oral cavity cancer are detected at later stages. It is generally accepted that the differential diagnosis of OCSCC is usually difficult and requires expertise and experience. Diagnosis from biopsy tissue is a complex process, and it is slow, costly, and prone to human error. To overcome these problems, a computer-aided diagnosis (CAD) approach was proposed in this work. A dataset comprising two categories, normal epithelium of the oral cavity (NEOR) and squamous cell carcinoma of the oral cavity (OSCC), was used. Feature extraction was performed from this dataset using four deep learning (DL) models (VGG16, AlexNet, ResNet50, and Inception V3) to realize artificial intelligence of medial things (AIoMT). Binary Particle Swarm Optimization (BPSO) was used to select the best features. The effects of Reinhard stain normalization on performance were also investigated. After the best features were extracted and selected, they were classified using the XGBoost. The best classification accuracy of 96.3% was obtained when using Inception V3 with BPSO. This approach significantly contributes to improving the diagnostic efficiency of OCSCC patients using histopathological images while reducing diagnostic costs.
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spelling pubmed-95466602022-10-08 Diagnosis of Oral Squamous Cell Carcinoma Using Deep Neural Networks and Binary Particle Swarm Optimization on Histopathological Images: An AIoMT Approach Deif, Mohanad A. Attar, Hani Amer, Ayman Elhaty, Ismail A. Khosravi, Mohammad R. Solyman, Ahmed A. A. Comput Intell Neurosci Research Article Overall prediction of oral cavity squamous cell carcinoma (OCSCC) remains inadequate, as more than half of patients with oral cavity cancer are detected at later stages. It is generally accepted that the differential diagnosis of OCSCC is usually difficult and requires expertise and experience. Diagnosis from biopsy tissue is a complex process, and it is slow, costly, and prone to human error. To overcome these problems, a computer-aided diagnosis (CAD) approach was proposed in this work. A dataset comprising two categories, normal epithelium of the oral cavity (NEOR) and squamous cell carcinoma of the oral cavity (OSCC), was used. Feature extraction was performed from this dataset using four deep learning (DL) models (VGG16, AlexNet, ResNet50, and Inception V3) to realize artificial intelligence of medial things (AIoMT). Binary Particle Swarm Optimization (BPSO) was used to select the best features. The effects of Reinhard stain normalization on performance were also investigated. After the best features were extracted and selected, they were classified using the XGBoost. The best classification accuracy of 96.3% was obtained when using Inception V3 with BPSO. This approach significantly contributes to improving the diagnostic efficiency of OCSCC patients using histopathological images while reducing diagnostic costs. Hindawi 2022-09-30 /pmc/articles/PMC9546660/ /pubmed/36210968 http://dx.doi.org/10.1155/2022/6364102 Text en Copyright © 2022 Mohanad A. Deif et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Deif, Mohanad A.
Attar, Hani
Amer, Ayman
Elhaty, Ismail A.
Khosravi, Mohammad R.
Solyman, Ahmed A. A.
Diagnosis of Oral Squamous Cell Carcinoma Using Deep Neural Networks and Binary Particle Swarm Optimization on Histopathological Images: An AIoMT Approach
title Diagnosis of Oral Squamous Cell Carcinoma Using Deep Neural Networks and Binary Particle Swarm Optimization on Histopathological Images: An AIoMT Approach
title_full Diagnosis of Oral Squamous Cell Carcinoma Using Deep Neural Networks and Binary Particle Swarm Optimization on Histopathological Images: An AIoMT Approach
title_fullStr Diagnosis of Oral Squamous Cell Carcinoma Using Deep Neural Networks and Binary Particle Swarm Optimization on Histopathological Images: An AIoMT Approach
title_full_unstemmed Diagnosis of Oral Squamous Cell Carcinoma Using Deep Neural Networks and Binary Particle Swarm Optimization on Histopathological Images: An AIoMT Approach
title_short Diagnosis of Oral Squamous Cell Carcinoma Using Deep Neural Networks and Binary Particle Swarm Optimization on Histopathological Images: An AIoMT Approach
title_sort diagnosis of oral squamous cell carcinoma using deep neural networks and binary particle swarm optimization on histopathological images: an aiomt approach
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9546660/
https://www.ncbi.nlm.nih.gov/pubmed/36210968
http://dx.doi.org/10.1155/2022/6364102
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