@inproceedings{719e90a3573f46299073632fd2886bba,
title = "A Performance Based Study on Deep Learning Algorithms in the Effective Prediction of Breast Cancer",
abstract = "Breast Cancer is one of the leading causes of death worldwide. Early detection is very important in increasing survival rates. Intensive research is therefore done to improve early detection of such cancers through the use of available technology. This includes various image processing techniques and general machine learning. However, the reported accuracy for many of these studies was often not at the desirable level. Deep Learning based techniques are a promising approach for the early detection of Breast Cancer. We have therefore done a comparative analysis of seven Deep Learning techniques applied to the Wisconsin Breast Cancer (Diagnostic) Dataset. Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) were proven to be the most effective algorithms as these have demonstrated good results for the majority of performance indicators used in this study, including an accuracy of over 99 percent.",
keywords = "breast cancer, deep learning, GRU, health informatics, LSTM, machine learning",
author = "Pronab Ghosh and Sami Azam and Hasib, {Khan Md} and Asif Karim and Mirjam Jonkman and Adnan Anwar",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 2021 International Joint Conference on Neural Networks, IJCNN 2021 ; Conference date: 18-07-2021 Through 22-07-2021",
year = "2021",
month = jul,
day = "18",
doi = "10.1109/IJCNN52387.2021.9534293",
language = "English",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "IEEE, Institute of Electrical and Electronics Engineers",
booktitle = "IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings",
address = "United States",
}