Smarter Cancer Detection Through Neural Network Classification of High-Resolution X-ray Diffraction Tissue Scans

Abstract

A need exists for an intraoperative margin assessment tool that can improve the efficiency of pathological assessment by efficient classification of excised tissue boundaries. To address this need, we have developed a system that combines x-ray diffraction imaging with a neural network classifier to achieve high-resolution, high-accuracy cancer imaging. The system’s x-ray diffraction imaging component is constructed using a Coded Aperture Coherent Scatter Spectral Imaging (CACSSI) arrangement, which provides tissue-specific molecular-scale contrast, and processes this data through a multi-layer perceptron neural network. Our current system shows upwards of 84% classification accuracy compared to the accepted standard of pathological assessment. Compared to our previous system, this is a 10% improvement in classification accuracy and is achieved in less than a third of the time needed by our previous system.