Automatic detection of pneumonia in chest X-rays using Lobe deep residual network
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..
Medienart: |
Preprint |
---|
Erscheinungsjahr: |
2021 |
---|---|
Erschienen: |
2021 |
Enthalten in: |
Preprints.org - (2021) vom: 08. Apr. Zur Gesamtaufnahme - year:2021 |
---|
Sprache: |
Englisch |
---|
Beteiligte Personen: |
Kvak, Daniel [VerfasserIn] |
---|
Links: |
Volltext [kostenfrei] |
---|
Themen: |
---|
doi: |
10.20944/preprints202104.0221.v1 |
---|
funding: |
|
---|---|
Förderinstitution / Projekttitel: |
|
PPN (Katalog-ID): |
preprintsorg020309872 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | preprintsorg020309872 | ||
003 | DE-627 | ||
005 | 20230429203749.0 | ||
007 | cr uuu---uuuuu | ||
008 | 210408s2021 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.20944/preprints202104.0221.v1 |2 doi | |
035 | |a (DE-627)preprintsorg020309872 | ||
035 | |a (preprintsorg)10.20944/preprints202104.0221 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
100 | 1 | |a Kvak, Daniel |e verfasserin |4 aut | |
245 | 1 | 0 | |a Automatic detection of pneumonia in chest X-rays using Lobe deep residual network |
264 | 1 | |c 2021 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
520 | |a 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. | ||
650 | 4 | |a Mathematics & Computer Science | |
650 | 4 | |a 000,510 | |
700 | 1 | |a Kvaková, Karolína |e verfasserin |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Preprints.org |g (2021) vom: 08. Apr. |
773 | 1 | 8 | |g year:2021 |g day:08 |g month:04 |
856 | 4 | 0 | |u http://dx.doi.org/10.20944/preprints202104.0221.v1 |z kostenfrei |3 Volltext |
912 | |a preprintsorg | ||
912 | |a SSG-OLC-PHA | ||
951 | |a AR | ||
952 | |j 2021 |b 08 |c 04 |