Explainable Ensemble Machine Learning for Breast Cancer Diagnosis based on Ultrasound Image Texture Features. (arXiv:2201.07227v1 [eess.IV])

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 …

arxiv cancer diagnosis ensemble learning machine machine learning