Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Ziyambe, Blessed, Yahya, Abid, Mushiri, Tawanda, Tariq, Muhammad Usman, Abbas, Qaisar, Babar, Muhammad, Albathan, Mubarak, Asim, Muhammad, Hussain, Ayyaz, Jabbar, Sohail
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
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
_version_ 1785048444185870336
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
work_keys_str_mv AT ziyambeblessed adeeplearningframeworkforthepredictionanddiagnosisofovariancancerinpreandpostmenopausalwomen
AT yahyaabid adeeplearningframeworkforthepredictionanddiagnosisofovariancancerinpreandpostmenopausalwomen
AT mushiritawanda adeeplearningframeworkforthepredictionanddiagnosisofovariancancerinpreandpostmenopausalwomen
AT tariqmuhammadusman adeeplearningframeworkforthepredictionanddiagnosisofovariancancerinpreandpostmenopausalwomen
AT abbasqaisar adeeplearningframeworkforthepredictionanddiagnosisofovariancancerinpreandpostmenopausalwomen
AT babarmuhammad adeeplearningframeworkforthepredictionanddiagnosisofovariancancerinpreandpostmenopausalwomen
AT albathanmubarak adeeplearningframeworkforthepredictionanddiagnosisofovariancancerinpreandpostmenopausalwomen
AT asimmuhammad adeeplearningframeworkforthepredictionanddiagnosisofovariancancerinpreandpostmenopausalwomen
AT hussainayyaz adeeplearningframeworkforthepredictionanddiagnosisofovariancancerinpreandpostmenopausalwomen
AT jabbarsohail adeeplearningframeworkforthepredictionanddiagnosisofovariancancerinpreandpostmenopausalwomen
AT ziyambeblessed deeplearningframeworkforthepredictionanddiagnosisofovariancancerinpreandpostmenopausalwomen
AT yahyaabid deeplearningframeworkforthepredictionanddiagnosisofovariancancerinpreandpostmenopausalwomen
AT mushiritawanda deeplearningframeworkforthepredictionanddiagnosisofovariancancerinpreandpostmenopausalwomen
AT tariqmuhammadusman deeplearningframeworkforthepredictionanddiagnosisofovariancancerinpreandpostmenopausalwomen
AT abbasqaisar deeplearningframeworkforthepredictionanddiagnosisofovariancancerinpreandpostmenopausalwomen
AT babarmuhammad deeplearningframeworkforthepredictionanddiagnosisofovariancancerinpreandpostmenopausalwomen
AT albathanmubarak deeplearningframeworkforthepredictionanddiagnosisofovariancancerinpreandpostmenopausalwomen
AT asimmuhammad deeplearningframeworkforthepredictionanddiagnosisofovariancancerinpreandpostmenopausalwomen
AT hussainayyaz deeplearningframeworkforthepredictionanddiagnosisofovariancancerinpreandpostmenopausalwomen
AT jabbarsohail deeplearningframeworkforthepredictionanddiagnosisofovariancancerinpreandpostmenopausalwomen