Cargando…
LCDAE: Data Augmented Ensemble Framework for Lung Cancer Classification
Objective: The only possible solution to increase the patients’ fatality rate is lung cancer early-stage detection. Recently, deep learning techniques became the most promising methods in medical image analysis compared with other numerous computer-aided diagnostic techniques. However, deep learning...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9511553/ https://www.ncbi.nlm.nih.gov/pubmed/36148908 http://dx.doi.org/10.1177/15330338221124372 |
_version_ | 1784797665496662016 |
---|---|
author | Ren, Zeyu Zhang, Yudong Wang, Shuihua |
author_facet | Ren, Zeyu Zhang, Yudong Wang, Shuihua |
author_sort | Ren, Zeyu |
collection | PubMed |
description | Objective: The only possible solution to increase the patients’ fatality rate is lung cancer early-stage detection. Recently, deep learning techniques became the most promising methods in medical image analysis compared with other numerous computer-aided diagnostic techniques. However, deep learning models always get lower performance when the model is overfitting. Methods: We present a Lung Cancer Data Augmented Ensemble (LCDAE) framework to solve the overfitting and lower performance problems in the lung cancer classification tasks. The LCDAE has 3 parts: The Lung Cancer Deep Convolutional GAN, which can synthesize images of lung cancer; A Data Augmented Ensemble model (DA-ENM), which ensembled 6 fine-tuned transfer learning models for training, testing, and validating on a lung cancer dataset; The third part is a Hybrid Data Augmentation (HDA) which combines all the data augmentation techniques in the LCDAE. Results: By comparing with existing state-of-the-art methods, the LCDAE obtains the best accuracy of 99.99%, the precision of 99.99%, and the F1-score of 99.99%. Conclusion: Our proposed LCDAE can overcome the overfitting issue for the lung cancer classification tasks by applying different data augmentation techniques, our method also has the best performance compared to state-of-the-art approaches. |
format | Online Article Text |
id | pubmed-9511553 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-95115532022-09-27 LCDAE: Data Augmented Ensemble Framework for Lung Cancer Classification Ren, Zeyu Zhang, Yudong Wang, Shuihua Technol Cancer Res Treat Novel Applications of Artificial Intelligence in Cancer Research Objective: The only possible solution to increase the patients’ fatality rate is lung cancer early-stage detection. Recently, deep learning techniques became the most promising methods in medical image analysis compared with other numerous computer-aided diagnostic techniques. However, deep learning models always get lower performance when the model is overfitting. Methods: We present a Lung Cancer Data Augmented Ensemble (LCDAE) framework to solve the overfitting and lower performance problems in the lung cancer classification tasks. The LCDAE has 3 parts: The Lung Cancer Deep Convolutional GAN, which can synthesize images of lung cancer; A Data Augmented Ensemble model (DA-ENM), which ensembled 6 fine-tuned transfer learning models for training, testing, and validating on a lung cancer dataset; The third part is a Hybrid Data Augmentation (HDA) which combines all the data augmentation techniques in the LCDAE. Results: By comparing with existing state-of-the-art methods, the LCDAE obtains the best accuracy of 99.99%, the precision of 99.99%, and the F1-score of 99.99%. Conclusion: Our proposed LCDAE can overcome the overfitting issue for the lung cancer classification tasks by applying different data augmentation techniques, our method also has the best performance compared to state-of-the-art approaches. SAGE Publications 2022-09-23 /pmc/articles/PMC9511553/ /pubmed/36148908 http://dx.doi.org/10.1177/15330338221124372 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any 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 | Novel Applications of Artificial Intelligence in Cancer Research Ren, Zeyu Zhang, Yudong Wang, Shuihua LCDAE: Data Augmented Ensemble Framework for Lung Cancer Classification |
title | LCDAE: Data Augmented Ensemble Framework for Lung Cancer Classification |
title_full | LCDAE: Data Augmented Ensemble Framework for Lung Cancer Classification |
title_fullStr | LCDAE: Data Augmented Ensemble Framework for Lung Cancer Classification |
title_full_unstemmed | LCDAE: Data Augmented Ensemble Framework for Lung Cancer Classification |
title_short | LCDAE: Data Augmented Ensemble Framework for Lung Cancer Classification |
title_sort | lcdae: data augmented ensemble framework for lung cancer classification |
topic | Novel Applications of Artificial Intelligence in Cancer Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9511553/ https://www.ncbi.nlm.nih.gov/pubmed/36148908 http://dx.doi.org/10.1177/15330338221124372 |
work_keys_str_mv | AT renzeyu lcdaedataaugmentedensembleframeworkforlungcancerclassification AT zhangyudong lcdaedataaugmentedensembleframeworkforlungcancerclassification AT wangshuihua lcdaedataaugmentedensembleframeworkforlungcancerclassification |