Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Pre-processing methods in chest X-ray image classification
77
Zitationen
4
Autoren
2022
Jahr
Abstract
BACKGROUND: The SARS-CoV-2 pandemic began in early 2020, paralyzing human life all over the world and threatening our security. Thus, the need for an effective, novel approach to diagnosing, preventing, and treating COVID-19 infections became paramount. METHODS: This article proposes a machine learning-based method for the classification of chest X-ray images. We also examined some of the pre-processing methods such as thresholding, blurring, and histogram equalization. RESULTS: We found the F1-score results rose to 97%, 96%, and 99% for the three analyzed classes: healthy, COVID-19, and pneumonia, respectively. CONCLUSION: Our research provides proof that machine learning can be used to support medics in chest X-ray classification and improving pre-processing leads to improvements in accuracy, precision, recall, and F1-scores.
Ähnliche Arbeiten
La certeza de lo impredecible: Cultura Educación y Sociedad en tiempos de COVID19
2020 · 19.307 Zit.
A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper)
2024 · 14.302 Zit.
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
2018 · 8.794 Zit.
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
2021 · 7.406 Zit.
scikit-image: image processing in Python
2014 · 6.840 Zit.