التفاصيل البيبلوغرافية
| العنوان: |
Federated Learning Approach with Pre-Trained Deep Learning Models for COVID-19 Detection from Unsegmented CT images |
| المؤلفون: |
Lucian Mihai Florescu, Costin Teodor Streba, Mircea-Sebastian Şerbănescu, Mădălin Mămuleanu, Dan Nicolae Florescu, Rossy Vlăduţ Teică, Raluca Elena Nica, Ioana Andreea Gheonea |
| المصدر: |
Life; Volume 12; Issue 7; Pages: 958 |
| بيانات النشر: |
Multidisciplinary Digital Publishing Institute |
| سنة النشر: |
2022 |
| المجموعة: |
MDPI Open Access Publishing |
| مصطلحات موضوعية: |
federated learning, COVID-19, computed tomography |
| جغرافية الموضوع: |
agris |
| الوصف: |
(1) Background: Coronavirus disease 2019 (COVID-19) is an infectious disease caused by SARS-CoV-2. Reverse transcription polymerase chain reaction (RT-PCR) remains the current gold standard for detecting SARS-CoV-2 infections in nasopharyngeal swabs. In Romania, the first reported patient to have contracted COVID-19 was officially declared on 26 February 2020. (2) Methods: This study proposes a federated learning approach with pre-trained deep learning models for COVID-19 detection. Three clients were locally deployed with their own dataset. The goal of the clients was to collaborate in order to obtain a global model without sharing samples from the dataset. The algorithm we developed was connected to our internal picture archiving and communication system and, after running backwards, it encountered chest CT changes suggestive for COVID-19 in a patient investigated in our medical imaging department on the 28 January 2020. (4) Conclusions: Based on our results, we recommend using an automated AI-assisted software in order to detect COVID-19 based on the lung imaging changes as an adjuvant diagnostic method to the current gold standard (RT-PCR) in order to greatly enhance the management of these patients and also limit the spread of the disease, not only to the general population but also to healthcare professionals. |
| نوع الوثيقة: |
text |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| Relation: |
Radiobiology and Nuclear Medicine; https://dx.doi.org/10.3390/life12070958 |
| DOI: |
10.3390/life12070958 |
| الاتاحة: |
https://doi.org/10.3390/life12070958 |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ |
| رقم الانضمام: |
edsbas.35DAA882 |
| قاعدة البيانات: |
BASE |