Breast Cancer Prediction Using Stacked GRU-LSTM-BRNN

Shawni Dutta,Jyotsna Kumar Mandal,Tai Hoon Kim,Samir Kumar Bandyopadhyay

doi:10.2478/acss-2020-0018

Shawni Dutta, Jyotsna Kumar Mandal + Show 2 more

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https://doi.org/10.2478/acss-2020-0018

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Abstract

Abstract Breast Cancer diagnosis is one of the most studied problems in the medical domain. Cancer diagnosis has been studied extensively, which instantiates the need for early prediction of cancer disease. To obtain advance prediction, health records are exploited and given as input to an automated system. The paper focuses on constructing an automated system by employing deep learning based recurrent neural network models. A stacked GRU-LSTM-BRNN is proposed in this paper that accepts health records of a patient for determining the possibility of being affected by breast cancer. The proposed model is compared against other baseline classifiers such as stacked simple-RNN model, stacked LSTM-RNN model, stacked GRU-RNN model. Comparative results obtained in this study indicate that the stacked GRU-LSTM-BRNN model yields better classification performance for predictions related to breast cancer disease.

Highlights

Adult female population is often affected by breast cancer (BC) which is one of the more commonly seen cancer types
Another Recurrent Neural Network (RNN) called Bidirectional RNN (BRNN) is designed to access input sequences whose starts and ends are known in advance
The present paper proposes a stacked Gated Recurrent Unit (GRU)-Long-short Term Memory (LSTM) based model that contains an alternate sequence of GRU and LSTM layers along with four dense layers

Summary

Introduction

Adult female population is often affected by breast cancer (BC) which is one of the more commonly seen cancer types. In India, an average 50 % of breast cancer cases are diagnosed at later stages such as III and IV This diagnosis rate reaches 12 % when it comes to the scenario of developed countries, such as the United States [2]. Knowing the influential factors can increase survival chances among women having breast cancer This will assist in defining early detection actions, counter measures in the healthcare field. Possible treatment of breast cancer includes various combinations of chemotherapy, surgery, radiation therapy, hormone therapy and targeted therapy via a multimodality approach Detection of this disease at an early stage will assist clinicians in suggesting probable treatments. Similar operations like the human brain can be simulated by incorporating memory cells to the neural network Another RNN called Bidirectional RNN (BRNN) is designed to access input sequences whose starts and ends are known in advance. While considering both past and future context of each sequence element into justification, one RNN processes the sequence from start to end, the other backwards from end to start [20]

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