PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Towards Clinical Practice: Design and Implementation of Convolutional Neural Network-Based Assistive Diagnosis System for COVID-19 Case Detection from Chest X-Ray Images
Version 1
: Received: 20 March 2022 / Approved: 22 March 2022 / Online: 22 March 2022 (02:19:50 CET)
How to cite:
Kvak, D.; Bendik, M.; Chromcova, A. Towards Clinical Practice: Design and Implementation of Convolutional Neural Network-Based Assistive Diagnosis System for COVID-19 Case Detection from Chest X-Ray Images. Preprints2022, 2022030288. https://doi.org/10.20944/preprints202203.0288.v1
Kvak, D.; Bendik, M.; Chromcova, A. Towards Clinical Practice: Design and Implementation of Convolutional Neural Network-Based Assistive Diagnosis System for COVID-19 Case Detection from Chest X-Ray Images. Preprints 2022, 2022030288. https://doi.org/10.20944/preprints202203.0288.v1
Kvak, D.; Bendik, M.; Chromcova, A. Towards Clinical Practice: Design and Implementation of Convolutional Neural Network-Based Assistive Diagnosis System for COVID-19 Case Detection from Chest X-Ray Images. Preprints2022, 2022030288. https://doi.org/10.20944/preprints202203.0288.v1
APA Style
Kvak, D., Bendik, M., & Chromcova, A. (2022). Towards Clinical Practice: Design and Implementation of Convolutional Neural Network-Based Assistive Diagnosis System for COVID-19 Case Detection from Chest X-Ray Images. Preprints. https://doi.org/10.20944/preprints202203.0288.v1
Chicago/Turabian Style
Kvak, D., Marian Bendik and Anna Chromcova. 2022 "Towards Clinical Practice: Design and Implementation of Convolutional Neural Network-Based Assistive Diagnosis System for COVID-19 Case Detection from Chest X-Ray Images" Preprints. https://doi.org/10.20944/preprints202203.0288.v1
Abstract
One of the critical tools for early detection and subsequent evaluation of the incidence of lung diseases is chest radiography. This study presents a real-world implementation of a convolutional neural network (CNN) based Carebot Covid app to detect COVID-19 from chest X-ray (CXR) images. Our proposed model takes the form of a simple and intuitive application. Used CNN can be deployed as a STOW-RS prediction endpoint for direct implementation into DICOM viewers. The results of this study show that the deep learning model based on DenseNet and ResNet architecture can detect SARS-CoV-2 from CXR images with precision of 0.981, recall of 0.962 and AP of 0.993.
Keywords
computer-aided detection; convolutional neural network; COVID-19; deep learning; image classification
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
