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A Deep Learning Framework for the Prediction and Diagnosis of Ovarian Cancer in Pre- and Post-Menopausal Women
Ovarian cancer ranks as the fifth leading cause of cancer-related mortality in women. Late-stage diagnosis (stages III and IV) is a major challenge due to the often vague and inconsistent initial symptoms. Current diagnostic methods, such as biomarkers, biopsy, and imaging tests, face limitations, i...
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/PMC10217055/ https://www.ncbi.nlm.nih.gov/pubmed/37238188 http://dx.doi.org/10.3390/diagnostics13101703 |
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author | Ziyambe, Blessed Yahya, Abid Mushiri, Tawanda Tariq, Muhammad Usman Abbas, Qaisar Babar, Muhammad Albathan, Mubarak Asim, Muhammad Hussain, Ayyaz Jabbar, Sohail |
author_facet | Ziyambe, Blessed Yahya, Abid Mushiri, Tawanda Tariq, Muhammad Usman Abbas, Qaisar Babar, Muhammad Albathan, Mubarak Asim, Muhammad Hussain, Ayyaz Jabbar, Sohail |
author_sort | Ziyambe, Blessed |
collection | PubMed |
description | Ovarian cancer ranks as the fifth leading cause of cancer-related mortality in women. Late-stage diagnosis (stages III and IV) is a major challenge due to the often vague and inconsistent initial symptoms. Current diagnostic methods, such as biomarkers, biopsy, and imaging tests, face limitations, including subjectivity, inter-observer variability, and extended testing times. This study proposes a novel convolutional neural network (CNN) algorithm for predicting and diagnosing ovarian cancer, addressing these limitations. In this paper, CNN was trained on a histopathological image dataset, divided into training and validation subsets and augmented before training. The model achieved a remarkable accuracy of 94%, with 95.12% of cancerous cases correctly identified and 93.02% of healthy cells accurately classified. The significance of this study lies in overcoming the challenges associated with the human expert examination, such as higher misclassification rates, inter-observer variability, and extended analysis times. This study presents a more accurate, efficient, and reliable approach to predicting and diagnosing ovarian cancer. Future research should explore recent advances in this field to enhance the effectiveness of the proposed method further. |
format | Online Article Text |
id | pubmed-10217055 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102170552023-05-27 A Deep Learning Framework for the Prediction and Diagnosis of Ovarian Cancer in Pre- and Post-Menopausal Women Ziyambe, Blessed Yahya, Abid Mushiri, Tawanda Tariq, Muhammad Usman Abbas, Qaisar Babar, Muhammad Albathan, Mubarak Asim, Muhammad Hussain, Ayyaz Jabbar, Sohail Diagnostics (Basel) Article Ovarian cancer ranks as the fifth leading cause of cancer-related mortality in women. Late-stage diagnosis (stages III and IV) is a major challenge due to the often vague and inconsistent initial symptoms. Current diagnostic methods, such as biomarkers, biopsy, and imaging tests, face limitations, including subjectivity, inter-observer variability, and extended testing times. This study proposes a novel convolutional neural network (CNN) algorithm for predicting and diagnosing ovarian cancer, addressing these limitations. In this paper, CNN was trained on a histopathological image dataset, divided into training and validation subsets and augmented before training. The model achieved a remarkable accuracy of 94%, with 95.12% of cancerous cases correctly identified and 93.02% of healthy cells accurately classified. The significance of this study lies in overcoming the challenges associated with the human expert examination, such as higher misclassification rates, inter-observer variability, and extended analysis times. This study presents a more accurate, efficient, and reliable approach to predicting and diagnosing ovarian cancer. Future research should explore recent advances in this field to enhance the effectiveness of the proposed method further. MDPI 2023-05-11 /pmc/articles/PMC10217055/ /pubmed/37238188 http://dx.doi.org/10.3390/diagnostics13101703 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 Ziyambe, Blessed Yahya, Abid Mushiri, Tawanda Tariq, Muhammad Usman Abbas, Qaisar Babar, Muhammad Albathan, Mubarak Asim, Muhammad Hussain, Ayyaz Jabbar, Sohail A Deep Learning Framework for the Prediction and Diagnosis of Ovarian Cancer in Pre- and Post-Menopausal Women |
title | A Deep Learning Framework for the Prediction and Diagnosis of Ovarian Cancer in Pre- and Post-Menopausal Women |
title_full | A Deep Learning Framework for the Prediction and Diagnosis of Ovarian Cancer in Pre- and Post-Menopausal Women |
title_fullStr | A Deep Learning Framework for the Prediction and Diagnosis of Ovarian Cancer in Pre- and Post-Menopausal Women |
title_full_unstemmed | A Deep Learning Framework for the Prediction and Diagnosis of Ovarian Cancer in Pre- and Post-Menopausal Women |
title_short | A Deep Learning Framework for the Prediction and Diagnosis of Ovarian Cancer in Pre- and Post-Menopausal Women |
title_sort | deep learning framework for the prediction and diagnosis of ovarian cancer in pre- and post-menopausal women |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10217055/ https://www.ncbi.nlm.nih.gov/pubmed/37238188 http://dx.doi.org/10.3390/diagnostics13101703 |
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