Using Generative Adversarial Networks and Parameter Optimization of Convolutional Neural Networks for Lung Tumor Classification
Cancer is the leading cause of death worldwide. Lung cancer, especially, caused the most death in 2018 according to the World Health Organization. Early diagnosis and treatment can considerably reduce mortality. To provide an efficient diagnosis, deep learning is overtaking conventional machine lear...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/2/480 |
| _version_ | 1827698243233906688 |
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| author | Chun-Hui Lin Cheng-Jian Lin Yu-Chi Li Shyh-Hau Wang |
| author_facet | Chun-Hui Lin Cheng-Jian Lin Yu-Chi Li Shyh-Hau Wang |
| author_sort | Chun-Hui Lin |
| collection | DOAJ |
| description | Cancer is the leading cause of death worldwide. Lung cancer, especially, caused the most death in 2018 according to the World Health Organization. Early diagnosis and treatment can considerably reduce mortality. To provide an efficient diagnosis, deep learning is overtaking conventional machine learning techniques and is increasingly being used in computer-aided design systems. However, a sparse medical data set and network parameter tuning process cause network training difficulty and cost longer experimental time. In the present study, the generative adversarial network was proposed to generate computed tomography images of lung tumors for alleviating the problem of sparse data. Furthermore, a parameter optimization method was proposed not only to improve the accuracy of lung tumor classification, but also reduce the experimental time. The experimental results revealed that the average accuracy can reach 99.86% after image augmentation and parameter optimization. |
| first_indexed | 2024-03-10T13:26:42Z |
| format | Article |
| id | doaj.art-00912a40a3cf48c795c299472b0bb60c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T13:26:42Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-00912a40a3cf48c795c299472b0bb60c2023-11-21T08:47:55ZengMDPI AGApplied Sciences2076-34172021-01-0111248010.3390/app11020480Using Generative Adversarial Networks and Parameter Optimization of Convolutional Neural Networks for Lung Tumor ClassificationChun-Hui Lin0Cheng-Jian Lin1Yu-Chi Li2Shyh-Hau Wang3Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, TaiwanDepartment of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung 411, TaiwanDepartment of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung 411, TaiwanDepartment of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, TaiwanCancer is the leading cause of death worldwide. Lung cancer, especially, caused the most death in 2018 according to the World Health Organization. Early diagnosis and treatment can considerably reduce mortality. To provide an efficient diagnosis, deep learning is overtaking conventional machine learning techniques and is increasingly being used in computer-aided design systems. However, a sparse medical data set and network parameter tuning process cause network training difficulty and cost longer experimental time. In the present study, the generative adversarial network was proposed to generate computed tomography images of lung tumors for alleviating the problem of sparse data. Furthermore, a parameter optimization method was proposed not only to improve the accuracy of lung tumor classification, but also reduce the experimental time. The experimental results revealed that the average accuracy can reach 99.86% after image augmentation and parameter optimization.https://www.mdpi.com/2076-3417/11/2/480lung cancerconvolutional neural networkgenerative adversarial networkimage augmentationparameter optimization |
| spellingShingle | Chun-Hui Lin Cheng-Jian Lin Yu-Chi Li Shyh-Hau Wang Using Generative Adversarial Networks and Parameter Optimization of Convolutional Neural Networks for Lung Tumor Classification Applied Sciences lung cancer convolutional neural network generative adversarial network image augmentation parameter optimization |
| title | Using Generative Adversarial Networks and Parameter Optimization of Convolutional Neural Networks for Lung Tumor Classification |
| title_full | Using Generative Adversarial Networks and Parameter Optimization of Convolutional Neural Networks for Lung Tumor Classification |
| title_fullStr | Using Generative Adversarial Networks and Parameter Optimization of Convolutional Neural Networks for Lung Tumor Classification |
| title_full_unstemmed | Using Generative Adversarial Networks and Parameter Optimization of Convolutional Neural Networks for Lung Tumor Classification |
| title_short | Using Generative Adversarial Networks and Parameter Optimization of Convolutional Neural Networks for Lung Tumor Classification |
| title_sort | using generative adversarial networks and parameter optimization of convolutional neural networks for lung tumor classification |
| topic | lung cancer convolutional neural network generative adversarial network image augmentation parameter optimization |
| url | https://www.mdpi.com/2076-3417/11/2/480 |
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