Article
Version 2
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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)
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.
Keywords
automatic detection; chest X-ray; convolutional neural network; COVID-19; deep learning; feature extraction; image classification; pneumonia
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Commenter: Daniel Kvak
Commenter's Conflict of Interests: Author
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.