التفاصيل البيبلوغرافية
| العنوان: |
Machine learning approaches for COVID-19 detection from chest X-ray imaging: A Systematic Review |
| المؤلفون: |
Arteaga-Arteaga, Harold Brayan, delaPava, Melissa, Mora-Rubio, Alejandro, Bravo-Ortíz, Mario Alejandro, Alzate-Grisales, Jesus Alejandro, Arias-Garzón, Daniel, López-Murillo, Luis Humberto, Buitrago-Carmona, Felipe, Villa-Pulgarín, Juan Pablo, Mercado-Ruiz, Esteban, Orozco-Arias, Simon, Hassaballah, M., de la Iglesia-Vaya, Maria, Cardona-Morales, Oscar, Tabares-Soto, Reinel |
| سنة النشر: |
2022 |
| المجموعة: |
Computer Science |
| مصطلحات موضوعية: |
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning |
| الوصف: |
There is a necessity to develop affordable, and reliable diagnostic tools, which allow containing the COVID-19 spreading. Machine Learning (ML) algorithms have been proposed to design support decision-making systems to assess chest X-ray images, which have proven to be useful to detect and evaluate disease progression. Many research articles are published around this subject, which makes it difficult to identify the best approaches for future work. This paper presents a systematic review of ML applied to COVID-19 detection using chest X-ray images, aiming to offer a baseline for researchers in terms of methods, architectures, databases, and current limitations. |
| نوع الوثيقة: |
Working Paper |
| URL الوصول: |
http://arxiv.org/abs/2206.05615 |
| رقم الانضمام: |
edsarx.2206.05615 |
| قاعدة البيانات: |
arXiv |