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Analysis of Cytology Pap Smear Images Based on Ensemble Deep Learning Approach

The fourth most prevalent cancer in women is cervical cancer, and early detection is crucial for effective treatment and prognostic prediction. Conventional cervical cancer screening and classifying methods are less reliable and accurate as they heavily rely on the expertise of a pathologist. As suc...

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Autores principales: Alsalatie, Mohammed, Alquran, Hiam, Mustafa, Wan Azani, Mohd Yacob, Yasmin, Ali Alayed, Asia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689383/
https://www.ncbi.nlm.nih.gov/pubmed/36428816
http://dx.doi.org/10.3390/diagnostics12112756
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author Alsalatie, Mohammed
Alquran, Hiam
Mustafa, Wan Azani
Mohd Yacob, Yasmin
Ali Alayed, Asia
author_facet Alsalatie, Mohammed
Alquran, Hiam
Mustafa, Wan Azani
Mohd Yacob, Yasmin
Ali Alayed, Asia
author_sort Alsalatie, Mohammed
collection PubMed
description The fourth most prevalent cancer in women is cervical cancer, and early detection is crucial for effective treatment and prognostic prediction. Conventional cervical cancer screening and classifying methods are less reliable and accurate as they heavily rely on the expertise of a pathologist. As such, colposcopy is an essential part of preventing cervical cancer. Computer-assisted diagnosis is essential for expanding cervical cancer screening because visual screening results in misdiagnosis and low diagnostic effectiveness due to doctors’ increased workloads. Classifying a single cervical cell will overwhelm the physicians, in addition to the existence of overlap between cervical cells, which needs efficient algorithms to separate each cell individually. Focusing on the whole image is the best way and an easy task for the diagnosis. Therefore, looking for new methods to diagnose the whole image is necessary and more accurate. However, existing recognition algorithms do not work well for whole-slide image (WSI) analysis, failing to generalize for different stains and imaging, and displaying subpar clinical-level verification. This paper describes the design of a full ensemble deep learning model for the automatic diagnosis of the WSI. The proposed network discriminates between four classes with high accuracy, reaching up to 99.6%. This work is distinct from existing research in terms of simplicity, accuracy, and speed. It focuses on the whole staining slice image, not on a single cell. The designed deep learning structure considers the slice image with overlapping and non-overlapping cervical cells.
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spelling pubmed-96893832022-11-25 Analysis of Cytology Pap Smear Images Based on Ensemble Deep Learning Approach Alsalatie, Mohammed Alquran, Hiam Mustafa, Wan Azani Mohd Yacob, Yasmin Ali Alayed, Asia Diagnostics (Basel) Article The fourth most prevalent cancer in women is cervical cancer, and early detection is crucial for effective treatment and prognostic prediction. Conventional cervical cancer screening and classifying methods are less reliable and accurate as they heavily rely on the expertise of a pathologist. As such, colposcopy is an essential part of preventing cervical cancer. Computer-assisted diagnosis is essential for expanding cervical cancer screening because visual screening results in misdiagnosis and low diagnostic effectiveness due to doctors’ increased workloads. Classifying a single cervical cell will overwhelm the physicians, in addition to the existence of overlap between cervical cells, which needs efficient algorithms to separate each cell individually. Focusing on the whole image is the best way and an easy task for the diagnosis. Therefore, looking for new methods to diagnose the whole image is necessary and more accurate. However, existing recognition algorithms do not work well for whole-slide image (WSI) analysis, failing to generalize for different stains and imaging, and displaying subpar clinical-level verification. This paper describes the design of a full ensemble deep learning model for the automatic diagnosis of the WSI. The proposed network discriminates between four classes with high accuracy, reaching up to 99.6%. This work is distinct from existing research in terms of simplicity, accuracy, and speed. It focuses on the whole staining slice image, not on a single cell. The designed deep learning structure considers the slice image with overlapping and non-overlapping cervical cells. MDPI 2022-11-10 /pmc/articles/PMC9689383/ /pubmed/36428816 http://dx.doi.org/10.3390/diagnostics12112756 Text en © 2022 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
Alsalatie, Mohammed
Alquran, Hiam
Mustafa, Wan Azani
Mohd Yacob, Yasmin
Ali Alayed, Asia
Analysis of Cytology Pap Smear Images Based on Ensemble Deep Learning Approach
title Analysis of Cytology Pap Smear Images Based on Ensemble Deep Learning Approach
title_full Analysis of Cytology Pap Smear Images Based on Ensemble Deep Learning Approach
title_fullStr Analysis of Cytology Pap Smear Images Based on Ensemble Deep Learning Approach
title_full_unstemmed Analysis of Cytology Pap Smear Images Based on Ensemble Deep Learning Approach
title_short Analysis of Cytology Pap Smear Images Based on Ensemble Deep Learning Approach
title_sort analysis of cytology pap smear images based on ensemble deep learning approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689383/
https://www.ncbi.nlm.nih.gov/pubmed/36428816
http://dx.doi.org/10.3390/diagnostics12112756
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