Chapter 4 - Transfer Learning and Supervised Classifier Based Prediction Model for Breast Cancer

Abstract

Breast cancer is one of the most common types of cancer among women. More than 1.5 million women each year are affected by breast cancer, which causes approximately 15% of all cancer deaths among women and the rate is increasing each year. Automatic detection of breast cancer could help to decrease the mortality rate by making diagnosis less time consuming and more accurate. Analysis of a breast cancer histopathology image is a challenging task. There have been many research studies on breast cancer tissue image classification. In this paper, we propose a machine learning model to automate the classification of benign and malignant tissue images. The best validation accuracy using this model for each of the magnification factor ranges from 92% to 94% and the best test accuracy ranges from 92% to 96%. The performance for each of the combination of feature extractor and classifier is also analyzed in terms of precision, recall, and f1-score, both in tabular form and graphically.