Computational studies on breast cancer analysis

Abstract

In women, breast cancer is a heterogeneous type of cancer; its classification is entirely established on clinical testimony and needs pathological advice for biological clarification. Various problems are associated with breast cancer research like its diagnosis, prognosis, redundancy prediction and survivability. Only relevant treatment according to cancer type is helpful. This paper deals with analysing the gene expression data for accurately identifying breast cancer type using deep learning model. It highlights how deep learning can be powerful tool for the cancer classification thus giving them required treatment. Our results show that Deep Learning can be an effective tool to interpret micro-array gene expression data and help in identifying breast cancer according to its ER status. The proposed model has shown effectiveness in terms of accuracy as compared to other machine learning algorithms.

Subject Classification:

• 68U07

Keywords:

• Breast cancer
• Estrogen receptor
• Progestogen receptor
• Human epidermal growth factor receptor
• Classification
• Deep learning