scholarly journals Going deeper: magnification‐invariant approach for breast cancer classification using histopathological images

2021 ◽  
Vol 15 (2) ◽  
pp. 151-164
Author(s):  
S. Alkassar ◽  
Bilal A. Jebur ◽  
Mohammed A. M. Abdullah ◽  
Joanna H. Al‐Khalidy ◽  
J. A. Chambers
Keyword(s):  
Breast Cancer ◽  
Cancer Classification ◽  
Breast Cancer Classification ◽  
Histopathological Images ◽  
Invariant Approach

Breast Cancer Classification from Histopathological Images Based on Improved Inception Model

2021 ◽  
Vol 58 (8) ◽  
pp. 0817001
Author(s):  
李赵旭 Li Zhaoxu ◽  
宋涛 Song Tao ◽  
葛梦飞 Ge Mengfei ◽  
刘嘉欣 Liu Jiaxin ◽  
王宏伟 Wang Hongwei ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Classification ◽  
Breast Cancer Classification ◽  
Histopathological Images

scholarly journals Breast Cancer Classification Using Deep Convolutional Neural Networks

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Jennifer K Chukwu ◽  
Faisal B. Sani ◽  
Aliyu S. Nuhu
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Cancer Classification ◽  
Computer Assisted ◽  
Final Decision ◽  
Deep Convolutional Neural Networks ◽  
Breast Cancer Classification ◽  
Histopathological Images

Breast cancer remains the primary causes of death for women and much effort has been depleted in the form of screening series for prevention. Given the exponential growth in the number of mammograms collected, computer-assisted diagnosis has become a necessity. Histopathological imaging is one of the methods for cancer diagnosis where Pathologists examine tissue cells under different microscopic standards but disagree on the final decision. In this context, the use of automatic image processing techniques resulting from deep learning denotes a promising avenue for assisting in the diagnosis of breast cancer. In this paper, an android software for breast cancer classification using deep learning approach based on a Convolutional Neural Network (CNN) was developed. The software aims to classify the breast tumors to benign or malignant. Experimental results on histopathological images using the BreakHis dataset shows that the DenseNet CNN model achieved high processing performances with 96% of accuracy in the breast cancer classification task when compared with state-of-the-art modelsKeywords— Breast cancer classification, Convolutional Neural Network (CNN), deep learning, DenseNet, histopathological images  

Sign in / Sign up

Export Citation Format

Share Document