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@article{1758316,
author = {Kvak, Daniel and Kvaková, Karolína},
article_location = {Basilej, Švýcarsko},
doi = {http://dx.doi.org/10.20944/preprints202104.0221.v2},
keywords = {automatic detection, chest X-ray, convolutional neural network, COVID-19, deep learning, feature extraction, image classification, pneumonia},
language = {eng},
journal = {Preprints},
title = {Automatic Detection of Pneumonia in Chest X-Rays using Lobe Deep Residual Network},
url = {https://www.preprints.org/manuscript/202104.0221/v2},
year = {2021}
}
TY - JFULL
ID - 1758316
AU - Kvak, Daniel - Kvaková, Karolína
PY - 2021
TI - Automatic Detection of Pneumonia in Chest X-Rays using Lobe Deep Residual Network
JF - Preprints
PB - MDPI
KW - automatic detection, chest X-ray, convolutional neural network, COVID-19, deep learning, feature extraction, image classification, pneumonia
UR - https://www.preprints.org/manuscript/202104.0221/v2
N2 - One of the critical tools for early detection and subsequent evaluation of the incidence of lung diseases is chest radiography. At a time when the speed and reliability of results, especially for COVID-19 positive patients, is important, the development of applications that would facilitate the work of untrained staff involved in the evaluation is also crucial. Our model takes the form of a simple and intuitive application, into which you only need to upload X-rays: tens or hundreds at once. In just a few seconds, the physician will determine the patient's diagnosis, including the percentage accuracy of the estimate. While the original idea was a mere binary classifier that could tell if a patient was suffering from pneumonia or not, in this paper we present a model that distinguishes between a bacterial disease, a viral infection, or a finding caused by COVID-19. The aim of this research is to demonstrate whether pneumonia can be detected or even spatially localized using a uniform, supervised classification.
ER -
KVAK, Daniel a Karolína KVAKOVÁ. Automatic Detection of Pneumonia in Chest X-Rays using Lobe Deep Residual Network. \textit{Preprints}. Basilej, Švýcarsko: MDPI, 2021, 10 s. Dostupné z: https://dx.doi.org/10.20944/preprints202104.0221.v2.
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