Optimization of Deep Learning Network Parameters Using Uniform Experimental Design for Breast Cancer Histopathological Image Classification

• Main Authors: Cheng-Jian Lin, Shiou-Yun Jeng
• Format: Article
• Language: English
• Published: MDPI AG 2020-09-01
• Series: Diagnostics
• Subjects: breast cancer; histopathology; deep learning; convolutional neural network; uniform experimental design
• Online Access: https://www.mdpi.com/2075-4418/10/9/662

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.