Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed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. Preprints 2022, 2022030288 (doi: 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 (doi: 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

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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