Breast Cancer Histopathological Image Classification via Deep Active Learning and Confidence Boosting

2018 ◽  
pp. 109-116 ◽  
Author(s):  
Baolin Du ◽  
Qi Qi ◽  
Han Zheng ◽  
Yue Huang ◽  
Xinghao Ding
Keyword(s):  
Breast Cancer ◽  
Active Learning ◽  
Image Classification ◽  
Histopathological Image ◽  
Histopathological Image Classification

Breast Cancer Histopathological Image Classification Based on Improved ResNeXt

2020 ◽  
Vol 57 (22) ◽  
pp. 221021
Author(s):  
牛学猛 Niu Xuemeng ◽  
吕晓琪 Lü Xiaoqi ◽  
谷宇 Gu Yu ◽  
张宝华 Zhang Baohua ◽  
张明 Zhang Ming ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Image Classification ◽  
Histopathological Image ◽  
Histopathological Image Classification

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

Diagnostics ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 662
Author(s):  
Cheng-Jian Lin ◽  
Shiou-Yun Jeng
Keyword(s):  
Breast Cancer ◽  
Experimental Design ◽  
Image Classification ◽  
Classification Accuracy ◽  
Medical Image Analysis ◽  
Breast Cancer Diagnosis ◽  
Health Concern ◽  
Cancer Type ◽  
Histopathological Image ◽  
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 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.

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