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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9689383 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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