TY  - JOUR
T1  - Software Engineering Model Based Early Detection Method of Breast
Cancer using Deep-Learning Framework
AU - Croock, Muayad S. AU - Korial, Ayad E. AU - Kareem, Tara F. AU - Hamad, Qusay Sh. AU - Abdulsaheb, Ghaidaa M. 
JO  - Journal of Engineering and Applied Sciences
VL  - 14
IS  - 16
SP  - 5775
EP  - 5781
PY  - 2019
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2019.5775.5781
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2019.5775.5781
KW  - software engineering
KW  -Deep-learning
KW  -breast cancer
KW  -mammography
KW  -incremental
KW  -employed
AB  - Recently, the serious diseases that can attach people have been increased in scary way. One of these
diseases is the breast cancer. This type of diseases affects the women in particular more than men. In this study,
a deep-learning framework is proposed for detecting the breast cancer in early stages based on mammography
images. It adopts the incremental development software process model as a part of software engineering in the
designing framework for more reliability and extendibility. The presented method has been established on
extracting the features of the employed images as a learning dataset for convolutional neural network inside
the deep-learning strategies. Different step algorithms have been used for performing the detection of the
standard benchmarks of breast cancer in the soft tissue, shown in the utilized mammography images. A real
dataset, collected from Baghdad hospital is considered and it is divided into 30% test and 70% training sets.
The obtained results show a high accuracy in terms of feature extraction of training set about 100% and breast
cancer detection from test set as a validation accuracy about 90%.
ER  -