Towards Clinical Practice: Design and Implementation of
Convolutional Neural Network-Based Assistive Diagnosis System
for COVID-19 Case Detection from Chest X-Ray Images

j 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

KVAK, Daniel; Anna CHROMCOVÁ a Marián BENDÍK

Základní údaje

Originální název

Towards Clinical Practice: Design and Implementation of Convolutional Neural Network-Based Assistive Diagnosis System for COVID-19 Case Detection from Chest X-Ray Images

Autoři

KVAK, Daniel; Anna CHROMCOVÁ a Marián BENDÍK

Vydání

arXiv preprint, 2022

Další údaje

Typ výsledku

Publikace v odborném periodiku – kromě recenzovaných typů article, review a letter

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Označené pro přenos do RIV

DOI

https://doi.org/10.48550/arXiv.2203.10596

Klíčová slova anglicky

computer-aided detection, convolutional neural network, COVID-19, deep learning, image classification

Příznaky

Mezinárodní význam

Změněno: 8. 11. 2022 17:06, Mgr. Daniel Kvak

Anotace

V originále

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.

Citovat

KVAK, Daniel; Anna CHROMCOVÁ a Marián BENDÍK. Towards Clinical Practice: Design and Implementation of Convolutional Neural Network-Based Assistive Diagnosis System for COVID-19 Case Detection from Chest X-Ray Images. arXiv preprint. 2022, 2203.10596, 19 s. Dostupné z: https://doi.org/10.48550/arXiv.2203.10596.

@article{1842317,
   author = {Kvak, Daniel and Chromcová, Anna and Bendík, Marián},
   article_number = {2203.10596},
   doi = {https://doi.org/10.48550/arXiv.2203.10596},
   keywords = {computer-aided detection, convolutional neural network, COVID-19, deep learning, image classification},
   journal = {arXiv preprint},
   title = {Towards Clinical Practice: Design and Implementation of Convolutional Neural Network-Based Assistive Diagnosis System for COVID-19 Case Detection from Chest X-Ray Images},
   url = {https://europepmc.org/article/PPR/PPR516837},
   year = {2022}
}

TY  - JFULL
ID  - 1842317
AU  - Kvak, Daniel - Chromcová, Anna - Bendík, Marián
PY  - 2022
TI  - Towards Clinical Practice: Design and Implementation of Convolutional Neural Network-Based Assistive Diagnosis System for COVID-19 Case Detection from Chest X-Ray Images
JF  - arXiv preprint
IS  - 2203.10596
KW  - computer-aided detection, convolutional neural network, COVID-19, deep learning, image classification
UR  - https://europepmc.org/article/PPR/PPR516837
N2  - 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.
ER  -

KVAK, Daniel; Anna CHROMCOVÁ a Marián BENDÍK. Towards Clinical Practice: Design and Implementation of Convolutional Neural Network-Based Assistive Diagnosis System for COVID-19 Case Detection from Chest X-Ray Images. \textit{arXiv preprint}. 2022, 2203.10596, 19 s. Dostupné z: https://doi.org/10.48550/arXiv.2203.10596.

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