Title: Breast cancer data classification using deep neural network

Authors: Vipul Sharma; Saumendra Kumar Mohapatra; Mihir Narayan Mohanty

Addresses: Department of Computer Science and Engineering, ITER, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India ' Department of Computer Science and Engineering, ITER, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India ' Department of Electronics and Communication Engineering, ITER, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India

Abstract: Artificial neural networks and their variants play an important role in the analysis and classification of different biomedical data. Deep learning is an advanced machine learning approach which has been used in many applications in the last few years. Worldwide breast cancer is a major disease for women; it is one of the most challenging jobs to detect at an early stage. The authors in this work have taken an attempt to classify the breast cancer data collected from the UCI machine learning repository. Malignant and benign two different types of breast cancer tumours are classified using deep neural network (DNN). Before classification two pre-processing steps are done for improving the accuracy. The correlation and one-hot encoding of the dataset was done for getting some relevant features that can be used as the input to the DNN. Around 94% of classification accuracy is achieved by using a six-layer DNN classifier. The result is also compared with some earlier works and it is found that the proposed classifier is providing better results as compared to others.

Keywords: artificial neural network; ANN; deep learning; deep neural network; DNN; breast cancer; classification.

DOI: 10.1504/IJISDC.2020.115169

International Journal of Intelligent Systems Design and Computing, 2020 Vol.3 No.2, pp.133 - 144

Received: 26 Mar 2020
Accepted: 02 Aug 2020

Published online: 12 May 2021 *

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