Multi-Class Breast Cancer Classification Using Ensemble of Pretrained models and Transfer Learning

Abstract

Aims: Early detection of breast cancer has reduced many deaths. Earlier CAD systems used to be the second opinion for radiologists and clinicians. Machine learning and deep learning have brought tremendous changes in medical diagnosis and imagining.

Background: Breast cancer is the most commonly occurring cancer in women and it is the second most common cancer overall. According to the 2018 statistics, there were over 2million cases all over the world. Belgium and Luxembourg have the highest rate of cancer.

Objective: A method for breast cancer detection has been proposed using Ensemble learning. 2- class and 8-class classification is performed.

Methods: To deal with imbalance classification, the authors have proposed an ensemble of pretrained models.

Results: 98.5% training accuracy and 89% of test accuracy are achieved on 8-class classification. Moreover, 99.1% and 98% train and test accuracy are achieved on 2 class classification.

Conclusion: it is found that there are high misclassifications in class DC when compared to the other classes, this is due to the imbalance in the dataset. In the future, one can increase the size of the datasets or use different methods. In implement this research work, authors have used 2 Nvidia Tesla V100 GPU's in google cloud platform.

Keywords: Machine learning; breast cancer classification; deep learning; densenet; ensemble learning; mobilenet; pyTorch; resnet; transfer learning.