Global Journal of Computer Science and Technology, C: Software & Data Engineering, Volume 22 Issue 2

© 2022. Maged Abdalla Helmy Abdou, Paulo Ferreira, Eric Jul & Tuyen Trung Truong. This research/review article is distributed under the terms of the Attribution-NonCommercial-NoDerivatives 4.0 International (CC BYNCND 4.0). You must give appropriate credit to authors and reference this article if parts of the article are reproduced in any manner. Applicable licensing terms are at https://creativecommons.org/licenses/by-nc-nd/4.0/. Global Journal of Computer Science and Technology: C Software & Data Engineering Volume 22 Issue 2 Version 1.0 Year 2022 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Online ISSN: 0975-4172 & Print ISSN: 0975-4350 Capillary X: A Software Design Pattern for Analyzing Medical Images in Real-time using Deep Learning By Maged Abdalla Helmy Abdou, Paulo Ferreira, Eric Jul & Tuyen Trung Truong University of Oslo 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 resourceintensive 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. GJCST-C Classification: DDC Code: 020.3 LCC Code: Z1006 CapillaryXASoftwareDesignPatternforAnalyzingMedicalImagesinRealtimeusingDeepLearning Strictly as per the compliance and regulations of: