Breast Cancer Prediction by Leveraging Machine Learning and Deep learning Techniques with Different Imaging Modalities
K Ranjini, S K Mouleeswaran · 2022 IEEE 7th International conference for Convergence in Technology (I2CT) · 2022
The high mortality rate due to breast cancer is a major concern that needs to be addressed critically. Early detection is the most necessary factor in cancer studies. Accurate clinical practice is required which can classify normal breast cells from abnormal cells. This classification requires various approaches in which analysis of imaging modalities and their results needs to be verified. Currently, available screening techniques are analyzed and their challenges and issues are presented. This work investigated some recently proposed approaches for breast cancer detection using Machine Learning (ML) and Deep Learning (DL) techniques and summarizes the contributions to this area. Convolutional Neural Network (CNN) has become a very popular medical images analysis technique due to the accuracy they have achieved in recent studies. This study has attempted to collect and compare the various classifiers used to detect breast cancer. The promising increase in the performance of advanced DL techniques are explored and the challenges and the approaches are discussed. This can result in tremendous improvements in performance and accuracy in the future.