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

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: Článek v odborném periodiku (nerecenzovaný)
• Utajení: není předmětem státního či obchodního tajemství
• WWW: URL
• Doi: http://dx.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

Anotace

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.