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

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dc.contributor.author Ghosh, Pronab
dc.contributor.author Azam, Sami
dc.contributor.author Hasib, Khan Md.
dc.contributor.author Karim, Asif
dc.contributor.author Jonkman, Mirjam
dc.contributor.author Anwar, Adnan
dc.date.accessioned 2022-04-04T03:48:12Z
dc.date.available 2022-04-04T03:48:12Z
dc.date.issued 2021-09-21
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/7683
dc.description.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. en_US
dc.language.iso en_US en_US
dc.publisher 2021 International Joint Conference on Neural Networks (IJCNN), IEEE en_US
dc.subject Deep learning en_US
dc.subject Image processing en_US
dc.subject Neural networks en_US
dc.subject Logic gates en_US
dc.subject Prediction algorithms en_US
dc.subject Breast cancer en_US
dc.subject Long short term memory en_US
dc.title A Performance Based Study on Deep Learning Algorithms in the Effective Prediction of Breast Cancer en_US
dc.type Article en_US


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