Detection of pneumonia using convolutional neural networks and deep learning

• Autorzy: Szepesi Patrik , Szilágyi László

Identyfikatory

DOI

10.1016/j.bbe.2022.08.001

• Języki publikacji: EN

Abstrakty

EN

The objective and automated detection of pneumonia represents a serious challenge in medical imaging, because the signs of the illness are not obvious in CT or X-ray scans. Further on, it is also an important task, since millions of people die of pneumonia every year. The main goal of this paper is to propose a solution for the above mentioned problem, using a novel deep neural network architecture. The proposed novelty consists in the use of dropout in the convolutional part of the network. The proposed method was trained and tested on a set of 5856 labeled images available at one of Kaggle’s many medical imaging challenges. The chest X-ray images (anterior-posterior) were selected from retrospective cohorts of pediatric patients, aged between one and five years, from Guangzhou Women and Children’s Medical Center, Guangzhou, China. Results achieved by our network would have placed first in the Kaggle competition with the following metrics: 97.2% accuracy, 97.3% recall, 97.4% precision and AUC ₌ 0:982, and they are competitive with current state-of-the-art solutions.

Słowa kluczowe

EN

convolutional neural network; deep learning; transfer learning; pneumonia detection; medical imaging

PL

sieć neuronowa konwolucyjna; uczenie głębokie; zapalenie płuc; obrazowanie medyczne

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2022
• Tom: Vol. 42, no. 3
• Strony: 1012--1022
• Opis fizyczny: Bibliogr. 45 poz., rys., tab., wykr.

Twórcy

autor

Szepesi Patrik

• Corvinus University of Budapest, Budapest, Hungary

autor

Szilágyi László

• Computational Intelligence Research Group (CIRG), Sapientia University of Transylvania, Şoseaua Sighişoarei 1/C, 540485 Tîrgu Mureş, Romania
• Physiological Controls Research Center, Óbuda University, Budapest, Hungary
• Biomatics and Applied Artificial Intelligence Institution, Óbuda University, Budapest, Hungary

Bibliografia

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