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Application of Transfer Learning and Feature Fusion Algorithms to Improve the Identification and Prediction Efficiency of Premature Ovarian Failure
Ultrasound imaging technology has the advantages of noninvasiveness, real-time, low price, and easy operation. It is one of the most used diagnostic tools for early detection and classification of premature ovarian failure. Although the rapid development of computer-aided diagnosis has provided a gr...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8977322/ https://www.ncbi.nlm.nih.gov/pubmed/35388326 http://dx.doi.org/10.1155/2022/3269692 |
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author | Zhang, Yuanyuan Hou, Jing Wang, Qiaoyun Hou, Aiqin Liu, Yanni |
author_facet | Zhang, Yuanyuan Hou, Jing Wang, Qiaoyun Hou, Aiqin Liu, Yanni |
author_sort | Zhang, Yuanyuan |
collection | PubMed |
description | Ultrasound imaging technology has the advantages of noninvasiveness, real-time, low price, and easy operation. It is one of the most used diagnostic tools for early detection and classification of premature ovarian failure. Although the rapid development of computer-aided diagnosis has provided a great help to the ultrasound diagnosis of premature ovarian failure, it still has many limitations and shortcomings, so this paper adopts transfer learning and feature fusion algorithms to improve the identification and prediction efficiency of premature ovarian failure. In this study, the POF group and the control group both adopted a unified scale. From the four aspects of sociological characteristics, past medical history, environmental factors, and living habits, a dedicated person asked and filled out the scale face to face. All patients participating in the experiment underwent ultrasound examinations. In this paper, the bottom-level feature fusion method is used to improve classification performance. The experiment uses 100 epochs. After each epoch training is completed, we used all the data and labels of the target domain to test. All experiments were performed five times, and the result is the average of five experiments. All the results of baseline and direct classification without migration use the average of five experimental results as the result. Migrating the features extracted by the InceptionV3 network has the best performance for predicting premature ovarian failure. Its classification accuracy is as high as 85.13%, and the F1 value is 0.78. The results show that the migration learning and feature fusion algorithms used in this paper can provide reliable predictive analysis and decision support for doctors in the diagnosis of premature ovarian failure. |
format | Online Article Text |
id | pubmed-8977322 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-89773222022-04-05 Application of Transfer Learning and Feature Fusion Algorithms to Improve the Identification and Prediction Efficiency of Premature Ovarian Failure Zhang, Yuanyuan Hou, Jing Wang, Qiaoyun Hou, Aiqin Liu, Yanni J Healthc Eng Research Article Ultrasound imaging technology has the advantages of noninvasiveness, real-time, low price, and easy operation. It is one of the most used diagnostic tools for early detection and classification of premature ovarian failure. Although the rapid development of computer-aided diagnosis has provided a great help to the ultrasound diagnosis of premature ovarian failure, it still has many limitations and shortcomings, so this paper adopts transfer learning and feature fusion algorithms to improve the identification and prediction efficiency of premature ovarian failure. In this study, the POF group and the control group both adopted a unified scale. From the four aspects of sociological characteristics, past medical history, environmental factors, and living habits, a dedicated person asked and filled out the scale face to face. All patients participating in the experiment underwent ultrasound examinations. In this paper, the bottom-level feature fusion method is used to improve classification performance. The experiment uses 100 epochs. After each epoch training is completed, we used all the data and labels of the target domain to test. All experiments were performed five times, and the result is the average of five experiments. All the results of baseline and direct classification without migration use the average of five experimental results as the result. Migrating the features extracted by the InceptionV3 network has the best performance for predicting premature ovarian failure. Its classification accuracy is as high as 85.13%, and the F1 value is 0.78. The results show that the migration learning and feature fusion algorithms used in this paper can provide reliable predictive analysis and decision support for doctors in the diagnosis of premature ovarian failure. Hindawi 2022-03-27 /pmc/articles/PMC8977322/ /pubmed/35388326 http://dx.doi.org/10.1155/2022/3269692 Text en Copyright © 2022 Yuanyuan Zhang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Zhang, Yuanyuan Hou, Jing Wang, Qiaoyun Hou, Aiqin Liu, Yanni Application of Transfer Learning and Feature Fusion Algorithms to Improve the Identification and Prediction Efficiency of Premature Ovarian Failure |
title | Application of Transfer Learning and Feature Fusion Algorithms to Improve the Identification and Prediction Efficiency of Premature Ovarian Failure |
title_full | Application of Transfer Learning and Feature Fusion Algorithms to Improve the Identification and Prediction Efficiency of Premature Ovarian Failure |
title_fullStr | Application of Transfer Learning and Feature Fusion Algorithms to Improve the Identification and Prediction Efficiency of Premature Ovarian Failure |
title_full_unstemmed | Application of Transfer Learning and Feature Fusion Algorithms to Improve the Identification and Prediction Efficiency of Premature Ovarian Failure |
title_short | Application of Transfer Learning and Feature Fusion Algorithms to Improve the Identification and Prediction Efficiency of Premature Ovarian Failure |
title_sort | application of transfer learning and feature fusion algorithms to improve the identification and prediction efficiency of premature ovarian failure |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8977322/ https://www.ncbi.nlm.nih.gov/pubmed/35388326 http://dx.doi.org/10.1155/2022/3269692 |
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