Abstract:
Currently, breast cancer is a type of cancer where
most of the cancer patients suffer. Identifying the type of breast
cancer is an essential step to be done in drug discovery for breast
cancers. Thus, the treatments could address the key features of
cancer and successfully the breast cancer can be cured.Majority
of the related studies on cancer classifications are based on
clinical diagnosis, hence affected with restricted classifications.
Thus, gene expression data which has obtained via transcription
profiling on microarrays have been used as the input for the
classification of cancer in this study. The key goal of this study
was to research the existing methods of breast cancer
classifications and implement an efficient breast cancer
classification method based on deep learning using gene
expression data which overcomes the defects of existing
methodologies of breast cancer classification. Two different
deep learning architectures were implemented by this study
which is Convolutional Neural Network(CNN) and Deep Belief
Network(DBN) using Tensorflow framework to classify breast
cancers under the classification based on gene and protein
status. Finally, we compared the performance of those two
architectures with the deep learning architecture, autoencoder
which was implemented before in another study in classifying
breast cancer using gene expression data. The two proposed
architectures perform better than autoencoder with respect to
precision, recall, F1 score and accuracy. In conclusion, CNN is
the best supervised deep learning architecture which yielded an
accuracy of 63.4395% and DBN is the best unsupervised deep
learning architecture which yielded an accuracy of 63.3545%in
classifying breast cancers using gene expression data based on
gene and protein status.
