Towards an Informed CNN for Bone SR-microCT Image Classification with an Unsupervised Patched-based Image Clustering.

Abstract

Bone microscale differences cannot be readily recognizable to humans from Synchrotron Radiation micro-Computed Tomography (SR-microCT) images. Premises are possible with Deep Learning (DL) imaging analysis. Despite this, more attention to high-level features leads models to require help identifying relevant details to support a decision. Within this context, we propose a method for classifying healthy, osteoporotic, and COVID-19 femoral heads SR-microCT images informing a vgg16 about the most subtle microscale differences using unsupervised patched-based clustering. Our strategy allows achieving up to 9.8% accuracy improvement in classifying healthy from osteoporotic images over uninformed methods, while 59.1% of accuracy between osteoporosis and COVID-19.Clinical relevance-We established a starting point for classifying healthy, osteoporotic, and COVID-19 femoral heads from SR-microCTs with human non-discriminative features, with 60.91% accuracy in healthy-osteporotic image classification.