@Article{jai.2021.022433,
AUTHOR = {Asmaa E. E. Ali, Mofreh Mohamed Salem, Mahmoud Badway, Ali I. EL Desouky},
TITLE = {A Deep Learning Breast Cancer Prediction Framework},
JOURNAL = {Journal on Artificial Intelligence},
VOLUME = {3},
YEAR = {2021},
NUMBER = {3},
PAGES = {81--96},
URL = {http://www.techscience.com/jai/v3n3/46666},
ISSN = {2579-003X},
ABSTRACT = {Breast cancer (BrC) is now the world’s leading cause of death for 
women. Early detection and effective treatment of this disease are the only rescues 
to reduce BrC mortality. The prediction of BrC diseases is very difficult because 
it is not an individual disease but a mixture of various diseases. Many researchers 
have used different techniques such as classification, Machine Learning (ML), and 
Deep Learning (DL) of the prediction of the breast tumor into Benign and 
Malignant. However, still there is a scope to introduce appropriate techniques for 
developing and implementing a more effective diagnosis system. This paper 
proposes a DL prediction BrC framework that uses a selected Bidirectional 
Recurrent Neural Network (BRNN). An efficient fast and accurate optimizer is 
needed to train the neural network used. The more recent Dynamic Group-based 
Cooperative Optimization Group (DGCO) algorithm is modified MDGCO for this 
purpose. The Deep Learning Breast Cancer Prediction Framework (DLBCPF) 
includes four layers: preprocessing, feature selection, optimized Recurrent Neural 
Networks, and prediction. Four different Wisconsin BrC datasets are used to test 
the validity of the proposed framework and optimizer against others. The results 
obtained have shown the superiority of both the framework DLBCPF and the 
optimizer MDGCO when they are compared to others.},
DOI = {10.32604/jai.2021.022433}
}