Scopus Indexed Publications

Paper Details


Title
A Performance Based Study on Deep Learning Algorithms in the Effective Prediction of Breast Cancer
Author
Pronab Ghosh,
Email
pronab1712@gmail.com
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.

Keywords
breast cancer , deep learning , LSTM , GRU , health informatics , machine learning
Journal or Conference Name
2021 International Joint Conference on Neural Networks (IJCNN)
Publication Year
2021
Indexing
scopus