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

doi:10.1109/IJCNN52387.2021.9534293

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

Higher Degree by Research (HDR) Cohort

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper published in Proceedings › peer-review

48 Citations (Scopus)

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.

Original language	English
Title of host publication	IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings
Place of Publication	Piscataway, NJ
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Number of pages	8
ISBN (Electronic)	9780738133669
DOIs	https://doi.org/10.1109/IJCNN52387.2021.9534293
Publication status	Published - 18 Jul 2021
Event	2021 International Joint Conference on Neural Networks, IJCNN 2021 - Virtual, Shenzhen, China Duration: 18 Jul 2021 → 22 Jul 2021

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	2021-July

Conference

Conference	2021 International Joint Conference on Neural Networks, IJCNN 2021
Country/Territory	China
City	Virtual, Shenzhen
Period	18/07/21 → 22/07/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/IJCNN52387.2021.9534293

Cite this

Ghosh, P., Azam, S., Hasib, K. M., Karim, A., Jonkman, M., & Anwar, A. (2021). A Performance Based Study on Deep Learning Algorithms in the Effective Prediction of Breast Cancer. In IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings (Proceedings of the International Joint Conference on Neural Networks; Vol. 2021-July). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IJCNN52387.2021.9534293

Ghosh, Pronab ; Azam, Sami ; Hasib, Khan Md et al. / A Performance Based Study on Deep Learning Algorithms in the Effective Prediction of Breast Cancer. IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings. Piscataway, NJ : IEEE, Institute of Electrical and Electronics Engineers, 2021. (Proceedings of the International Joint Conference on Neural Networks).

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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.",

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Ghosh, P, Azam, S, Hasib, KM, Karim, A , Jonkman, M & Anwar, A 2021, A Performance Based Study on Deep Learning Algorithms in the Effective Prediction of Breast Cancer. in IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings. Proceedings of the International Joint Conference on Neural Networks, vol. 2021-July, IEEE, Institute of Electrical and Electronics Engineers, Piscataway, NJ, 2021 International Joint Conference on Neural Networks, IJCNN 2021, Virtual, Shenzhen, China, 18/07/21. https://doi.org/10.1109/IJCNN52387.2021.9534293

A Performance Based Study on Deep Learning Algorithms in the Effective Prediction of Breast Cancer. / Ghosh, Pronab; Azam, Sami; Hasib, Khan Md et al.
IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings. Piscataway, NJ: IEEE, Institute of Electrical and Electronics Engineers, 2021. (Proceedings of the International Joint Conference on Neural Networks; Vol. 2021-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper published in Proceedings › peer-review

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AU - Jonkman, Mirjam

AU - Anwar, Adnan

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AB - 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.

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Ghosh P, Azam S, Hasib KM, Karim A , Jonkman M, Anwar A. A Performance Based Study on Deep Learning Algorithms in the Effective Prediction of Breast Cancer. In IJCNN 2021 - International Joint Conference on Neural Networks, Proceedings. Piscataway, NJ: IEEE, Institute of Electrical and Electronics Engineers. 2021. (Proceedings of the International Joint Conference on Neural Networks). doi: 10.1109/IJCNN52387.2021.9534293

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

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