Explainable Ensemble Machine Learning for Breast Cancer Diagnosis based on Ultrasound Image Texture Features

TL;DR

This paper presents an explainable ensemble machine learning approach using texture features from ultrasound images to improve breast cancer diagnosis, balancing high accuracy with interpretability.

Contribution

The study introduces a novel explainable pipeline combining texture feature extraction with decision tree ensembles for breast cancer diagnosis from ultrasound images.

Findings

Achieves high predictive accuracy

Provides interpretable decision paths

Outperforms some deep learning models in explainability

Abstract

Image classification is widely used to build predictive models for breast cancer diagnosis. Most existing approaches overwhelmingly rely on deep convolutional networks to build such diagnosis pipelines. These model architectures, although remarkable in performance, are black-box systems that provide minimal insight into the inner logic behind their predictions. This is a major drawback as the explainability of prediction is vital for applications such as cancer diagnosis. In this paper, we address this issue by proposing an explainable machine learning pipeline for breast cancer diagnosis based on ultrasound images. We extract first- and second-order texture features of the ultrasound images and use them to build a probabilistic ensemble of decision tree classifiers. Each decision tree learns to classify the input ultrasound image by learning a set of robust decision thresholds for texture features of the image. The decision path of the model predictions can then be interpreted by decomposing the learned decision trees. Our results show that our proposed framework achieves high predictive performance while being explainable.