CapillaryX: A Software Design Pattern for Analyzing Medical Images in Real-time using Deep Learning

Abstract

Abstract Recent advances in digital imaging e g increased number of pixels captured have meant that the volume of data to be processed and analyzed from these images has also increased Deep learning algorithms are state-of-the-art for analyzing such images given their high accuracy when trained with a large data volume of data Nevertheless such analysis requires considerable computational power making such algorithms time- and resource-demanding Such high demands can be met by using third-party cloud service providers However analyzing medical images using such services raises several legal and privacy challenges and do not necessarily provide real-time results This paper provides a computing architecture that locally and in parallel can analyze medical images in real-time using deep learning thus avoiding the legal and privacy challenges stemming from uploading data to a third-party cloud provider To make local image processing efficient on modern multi-core processors we utilize parallel execution to offset the resource- intensive demands of deep neural networks We focus on a specific medical-industrial case study namely the quantifying of blood vessels in microcirculation images for which we have developed a working system It is currently used in an industrial clinical research setting as part of an e-health application Our results show that our system is approximately 78 faster than its serial system counterpart and 12 faster than a master-slave parallel system architecture