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GyneScan: An Improved Online Paradigm for Screening of Ovarian Cancer via Tissue Characterization
Ovarian cancer is the fifth highest cause of cancer in women and the leading cause of death from gynecological cancers. Accurate diagnosis of ovarian cancer from acquired images is dependent on the expertise and experience of ultrasonographers or physicians, and is therefore, associated with inter o...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4527478/ https://www.ncbi.nlm.nih.gov/pubmed/24325128 http://dx.doi.org/10.7785/tcrtexpress.2013.600273 |
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author | Acharya, U. Rajendra Sree, S. Vinitha Kulshreshtha, Sanjeev Molinari, Filippo Koh, Joel En Wei Saba, Luca Suri, Jasjit S. |
author_facet | Acharya, U. Rajendra Sree, S. Vinitha Kulshreshtha, Sanjeev Molinari, Filippo Koh, Joel En Wei Saba, Luca Suri, Jasjit S. |
author_sort | Acharya, U. Rajendra |
collection | PubMed |
description | Ovarian cancer is the fifth highest cause of cancer in women and the leading cause of death from gynecological cancers. Accurate diagnosis of ovarian cancer from acquired images is dependent on the expertise and experience of ultrasonographers or physicians, and is therefore, associated with inter observer variabilities. Computer Aided Diagnostic (CAD) techniques use a number of different data mining techniques to automatically predict the presence or absence of cancer, and therefore, are more reliable and accurate. A review of published literature in the field of CAD based ovarian cancer detection indicates that many studies use ultrasound images as the base for analysis. The key objective of this work is to propose an effective adjunct CAD technique called GyneScan for ovarian tumor detection in ultrasound images. In our proposed data mining framework, we extract several texture features based on first order statistics, Gray Level Co-occurrence Matrix and run length matrix. The significant features selected using t-test are then used to train and test several supervised learning based classifiers such as Probabilistic Neural Networks (PNN), Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbor (KNN), and Naïve Bayes (NB). We evaluated the developed framework using 1300 benign and 1300 malignant images. Using 11 significant features in KNN/PNN classifiers, we were able to achieve 100% classification accuracy, sensitivity, specificity, and positive predictive value in detecting ovarian tumor. Even though more validation using larger databases would better establish the robustness of our technique, the preliminary results are promising. This technique could be used as a reliable adjunct method to existing imaging modalities to provide a more confident second opinion on the presence/absence of ovarian tumor. |
format | Online Article Text |
id | pubmed-4527478 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-45274782015-12-14 GyneScan: An Improved Online Paradigm for Screening of Ovarian Cancer via Tissue Characterization Acharya, U. Rajendra Sree, S. Vinitha Kulshreshtha, Sanjeev Molinari, Filippo Koh, Joel En Wei Saba, Luca Suri, Jasjit S. Technol Cancer Res Treat Articles Ovarian cancer is the fifth highest cause of cancer in women and the leading cause of death from gynecological cancers. Accurate diagnosis of ovarian cancer from acquired images is dependent on the expertise and experience of ultrasonographers or physicians, and is therefore, associated with inter observer variabilities. Computer Aided Diagnostic (CAD) techniques use a number of different data mining techniques to automatically predict the presence or absence of cancer, and therefore, are more reliable and accurate. A review of published literature in the field of CAD based ovarian cancer detection indicates that many studies use ultrasound images as the base for analysis. The key objective of this work is to propose an effective adjunct CAD technique called GyneScan for ovarian tumor detection in ultrasound images. In our proposed data mining framework, we extract several texture features based on first order statistics, Gray Level Co-occurrence Matrix and run length matrix. The significant features selected using t-test are then used to train and test several supervised learning based classifiers such as Probabilistic Neural Networks (PNN), Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbor (KNN), and Naïve Bayes (NB). We evaluated the developed framework using 1300 benign and 1300 malignant images. Using 11 significant features in KNN/PNN classifiers, we were able to achieve 100% classification accuracy, sensitivity, specificity, and positive predictive value in detecting ovarian tumor. Even though more validation using larger databases would better establish the robustness of our technique, the preliminary results are promising. This technique could be used as a reliable adjunct method to existing imaging modalities to provide a more confident second opinion on the presence/absence of ovarian tumor. SAGE Publications 2014-12 /pmc/articles/PMC4527478/ /pubmed/24325128 http://dx.doi.org/10.7785/tcrtexpress.2013.600273 Text en © Adenine Press (2014) http://creativecommons.org/licenses/by-nc/3.0/ This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 3.0 License (http://www.creativecommons.org/licenses/by-nc/3.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Articles Acharya, U. Rajendra Sree, S. Vinitha Kulshreshtha, Sanjeev Molinari, Filippo Koh, Joel En Wei Saba, Luca Suri, Jasjit S. GyneScan: An Improved Online Paradigm for Screening of Ovarian Cancer via Tissue Characterization |
title | GyneScan: An Improved Online Paradigm for Screening of Ovarian Cancer via Tissue Characterization |
title_full | GyneScan: An Improved Online Paradigm for Screening of Ovarian Cancer via Tissue Characterization |
title_fullStr | GyneScan: An Improved Online Paradigm for Screening of Ovarian Cancer via Tissue Characterization |
title_full_unstemmed | GyneScan: An Improved Online Paradigm for Screening of Ovarian Cancer via Tissue Characterization |
title_short | GyneScan: An Improved Online Paradigm for Screening of Ovarian Cancer via Tissue Characterization |
title_sort | gynescan: an improved online paradigm for screening of ovarian cancer via tissue characterization |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4527478/ https://www.ncbi.nlm.nih.gov/pubmed/24325128 http://dx.doi.org/10.7785/tcrtexpress.2013.600273 |
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