A Performance Based Study on Deep Learning Algorithms in the Effective Prediction of Breast Cancer

Pronab Ghosh, Sami Azam, Khan Md Hasib, Asif Karim, Mirjam Jonkman, Adnan Anwar

Research output: Chapter in Book/Report/Conference proceedingConference Paper published in Proceedingspeer-review

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 andgeneral 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.

Original languageEnglish
Title of host publicationIJCNN 2021 - International Joint Conference on Neural Networks, Proceedings
Place of PublicationPiscataway, NJ
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9780738133669
DOIs
Publication statusPublished - 18 Jul 2021
Event2021 International Joint Conference on Neural Networks, IJCNN 2021 - Virtual, Shenzhen, China
Duration: 18 Jul 202122 Jul 2021

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2021-July

Conference

Conference2021 International Joint Conference on Neural Networks, IJCNN 2021
CountryChina
CityVirtual, Shenzhen
Period18/07/2122/07/21

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