Working Paper Article Version 2 This version is not peer-reviewed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 Kvak, D.; Kvaková, K. Automatic Detection of Pneumonia in Chest X-Rays using Lobe Deep Residual Network. Preprints 2021, 2021040221

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.

Subject Areas

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

Comments (1)

Comment 1
Received: 27 April 2021
Commenter: Daniel Kvak
Commenter's Conflict of Interests: Author
Comment: Dear editor,

We added chapters for the Background of the research, Data Augmentation, and Ethical Procedures. We created a new confusion matrix for the used dataset and added the evaluation metric formulas for P-R-F1 recall.
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