Optimization of Deep Learning Network Parameters Using Uniform Experimental Design for Breast Cancer Histopathological Image Classification
Breast cancer, a common cancer type, is a major health concern in women. Recently, researchers used convolutional neural networks (CNNs) for medical image analysis and demonstrated classification performance for breast cancer diagnosis from within histopathological image datasets. However, the param...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-09-01
|
| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/10/9/662 |
| _version_ | 1797554936014176256 |
|---|---|
| author | Cheng-Jian Lin Shiou-Yun Jeng |
| author_facet | Cheng-Jian Lin Shiou-Yun Jeng |
| author_sort | Cheng-Jian Lin |
| collection | DOAJ |
| description | Breast cancer, a common cancer type, is a major health concern in women. Recently, researchers used convolutional neural networks (CNNs) for medical image analysis and demonstrated classification performance for breast cancer diagnosis from within histopathological image datasets. However, the parameter settings of a CNN model are complicated, and using Breast Cancer Histopathological Database data for the classification is time-consuming. To overcome these problems, this study used a uniform experimental design (UED) and optimized the CNN parameters of breast cancer histopathological image classification. In UED, regression analysis was used to optimize the parameters. The experimental results indicated that the proposed method with UED parameter optimization provided 84.41% classification accuracy rate. In conclusion, the proposed method can improve the classification accuracy effectively, with results superior to those of other similar methods. |
| first_indexed | 2024-03-10T16:39:11Z |
| format | Article |
| id | doaj.art-d23c2151c7474f8e992720bc69a27367 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T16:39:11Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-d23c2151c7474f8e992720bc69a273672023-11-20T12:11:19ZengMDPI AGDiagnostics2075-44182020-09-0110966210.3390/diagnostics10090662Optimization of Deep Learning Network Parameters Using Uniform Experimental Design for Breast Cancer Histopathological Image ClassificationCheng-Jian Lin0Shiou-Yun Jeng1Department 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, TaiwanBreast cancer, a common cancer type, is a major health concern in women. Recently, researchers used convolutional neural networks (CNNs) for medical image analysis and demonstrated classification performance for breast cancer diagnosis from within histopathological image datasets. However, the parameter settings of a CNN model are complicated, and using Breast Cancer Histopathological Database data for the classification is time-consuming. To overcome these problems, this study used a uniform experimental design (UED) and optimized the CNN parameters of breast cancer histopathological image classification. In UED, regression analysis was used to optimize the parameters. The experimental results indicated that the proposed method with UED parameter optimization provided 84.41% classification accuracy rate. In conclusion, the proposed method can improve the classification accuracy effectively, with results superior to those of other similar methods.https://www.mdpi.com/2075-4418/10/9/662breast cancerhistopathologydeep learningconvolutional neural networkuniform experimental design |
| spellingShingle | Cheng-Jian Lin Shiou-Yun Jeng Optimization of Deep Learning Network Parameters Using Uniform Experimental Design for Breast Cancer Histopathological Image Classification Diagnostics breast cancer histopathology deep learning convolutional neural network uniform experimental design |
| title | Optimization of Deep Learning Network Parameters Using Uniform Experimental Design for Breast Cancer Histopathological Image Classification |
| title_full | Optimization of Deep Learning Network Parameters Using Uniform Experimental Design for Breast Cancer Histopathological Image Classification |
| title_fullStr | Optimization of Deep Learning Network Parameters Using Uniform Experimental Design for Breast Cancer Histopathological Image Classification |
| title_full_unstemmed | Optimization of Deep Learning Network Parameters Using Uniform Experimental Design for Breast Cancer Histopathological Image Classification |
| title_short | Optimization of Deep Learning Network Parameters Using Uniform Experimental Design for Breast Cancer Histopathological Image Classification |
| title_sort | optimization of deep learning network parameters using uniform experimental design for breast cancer histopathological image classification |
| topic | breast cancer histopathology deep learning convolutional neural network uniform experimental design |
| url | https://www.mdpi.com/2075-4418/10/9/662 |
| work_keys_str_mv | AT chengjianlin optimizationofdeeplearningnetworkparametersusinguniformexperimentaldesignforbreastcancerhistopathologicalimageclassification AT shiouyunjeng optimizationofdeeplearningnetworkparametersusinguniformexperimentaldesignforbreastcancerhistopathologicalimageclassification |