Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Automatic Detection of Pneumonia in Chest X-Rays using Lobe Deep Residual Network

Version 1 : Received: 7 April 2021 / Approved: 8 April 2021 / Online: 8 April 2021 (07:12:30 CEST)
Version 2 : Received: 27 April 2021 / Approved: 27 April 2021 / Online: 27 April 2021 (14:08:53 CEST)

How to cite: Kvak, D.; Kvaková, K. Automatic Detection of Pneumonia in Chest X-Rays using Lobe Deep Residual Network. Preprints 2021, 2021040221 (doi: 10.20944/preprints202104.0221.v1). Kvak, D.; Kvaková, K. Automatic Detection of Pneumonia in Chest X-Rays using Lobe Deep Residual Network. Preprints 2021, 2021040221 (doi: 10.20944/preprints202104.0221.v1).

Abstract

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.

Keywords

automatic detection; chest X-ray; convolutional neural network; COVID-19; deep learning; feature extraction; image classification; pneumonia

Subject

MATHEMATICS & COMPUTER SCIENCE, Algebra & Number Theory

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