Open AccessApplication of Deep Learning Method in Facilitating the Detection of Breast Cancer
Author(s) -
Azurah A. Samah,
Dewi Nasien,
Haslina Hashim,
Julia Sahar,
Hairudin Abdul Majid,
Yusliza Yusoff,
Zuraini Ali Shah
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/864/1/012079
Subject(s) - artificial intelligence , breast cancer , artificial neural network , deep learning , computer science , support vector machine , machine learning , classifier (uml) , deep neural networks , pattern recognition (psychology) , cancer , medicine
Breast cancer is a type of tumour that could be treated if the disease is identified at an earlier stage. Early diagnosis is crucial when it comes to reducing the mortality rate. In this study, deep neural network method is applied to facilitate the detection of breast cancer. The aim of this study is to implement deep neural network in breast cancer classification models that can produce high classification accuracy. Deep Neural Network (DNN) with multiple hidden layers was applied to learn deep features of the breast cancer data. Dataset used in this study was obtained from the UCI Machine Learning Repository which consists of Wisconsin Breast Cancer Dataset (WBCD) and used for the original and diagnostic dataset. The performance of the proposed DNN method was compared against previous machine learning classifier in terms of accuracy. From the results, the accuracy obtained for the original dataset was 97.14% and 97.66% for the diagnostic dataset, which is better than previous SVM method.