Title: Bio-medical analysis of breast cancer risk detection based on deep neural network

Authors: Nivaashini Mathappan; R.S. Soundariya; Aravindhraj Natarajan; Sathish Kumar Gopalan

Addresses: Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, India ' Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, India ' Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, India ' Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, India

Abstract: Breast tumour remains a most important reason of cancer fatality among women globally and most of them pass away due to delayed diagnosis. But premature recognition and anticipation can significantly diminish the chances of death. Risk detection of breast cancer is one of the major research areas in bioinformatics. Various experiments have been conceded to examine the fundamental grounds of breast tumour. Alternatively, it has already been verified that early diagnosis of tumour can give the longer survival chance to a patient. This paper aims at finding an efficient set of features for breast tumour prediction using deep learning approaches called restricted Boltzmann machine (RBM). The proposed framework diagnoses and analyses breast tumour patient's data with the help of deep neural network (DNN) classifier using the Wisconsin dataset of UCI machine learning repository and, thereafter assesses their performance in terms of measures like accuracy, precision, recall, F-measure, etc.

Keywords: breast tumour prediction; feature selection; restricted Boltzmann machine; RBM; deep neural network; DNN.

DOI: 10.1504/IJMEI.2020.10032878

International Journal of Medical Engineering and Informatics, 2020 Vol.12 No.6, pp.529 - 541

Received: 22 Aug 2017
Accepted: 27 Mar 2018

Published online: 06 Nov 2020 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article