Open Access
ARTICLE
A Deep Learning Breast Cancer Prediction Framework
Asmaa E. E. Ali*, Mofreh Mohamed Salem, Mahmoud Badway, Ali I. EL Desouky
Department of Computer Science, Faculty of Engineering, Mansoura University, Mansoura, 35111, Egypt
* Corresponding Author: Asmaa E. E. Ali. Email:
Journal on Artificial Intelligence 2021, 3(3), 81-96. https://doi.org/10.32604/jai.2021.022433
Received 07 August 2021; Accepted 15 December 2021; Issue published 25 January 2022
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.
Keywords
Cite This Article
A. E. E. Ali, M. M. Salem, M. Badway and A. I. E. Desouky, "A deep learning breast cancer prediction framework,"
Journal on Artificial Intelligence, vol. 3, no.3, pp. 81–96, 2021. https://doi.org/10.32604/jai.2021.022433