Multi-Feature Classification of Breast Cancer Histopathology Images: An Experimental Investigation in Machine Learning and Deep Learning Paradigm

Abstract

Abstract The existing practice for Breast Cancer (BC) characterization includes histopathological analysis, which is tedious and time-consuming due to massive data analysis. Further, such techniques are subjected to inter-and intra-observer variability due to the non-availability of skilled pathologists, particularly in low resource settings. Thus, we propose a multi-feature classification technique for risk stratification of BC in Histopathology Images (HI) using machine learning strategies and a Long Short-Term Memory (LSTM) based deep learning approach. Experiments are performed on a publicly available HI database from which a total of 658 image features are extracted, while 192 relevant features are obtained after feature selection using genetic algorithm. The highest accuracy of 99.85% using 192 features under the 5-fold data division protocol is obtained with the LSTM approach. The proposed framework for analyzing HI using multiple grayscale and color features showed promising results and can be an effective tool in the histopathology laboratory.

Keywords

• Intelligent laboratory analysis
• Breast cancer
• Feature fusion
• Machine learning
• Deep learning