عرض بسيط للتسجيلة

المؤلفMrad, Ilyes
المؤلفHamila, Ridha
المؤلفErbad, Aiman
المؤلفHamid, Tahir
المؤلفMazhar, Rashid
المؤلفAl-Emadi, Nasser
تاريخ الإتاحة2023-04-04T09:09:09Z
تاريخ النشر2021
اسم المنشورProceedings - European Workshop on Visual Information Processing, EUVIP
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1109/EUVIP50544.2021.9484001
معرّف المصادر الموحدhttp://hdl.handle.net/10576/41644
الملخصCOVID-19 is a virus that has infected more than one hundred and fifty million people and caused more than three million deaths by 13th of Mai 2021 and is having a catastrophic effect on the world population's safety. Therefore, early detection of infected people is essential to fight this pandemic and one of the main screening methods is radiological testing. The goal of this study is the usage of chest x-ray images (CXRs) to effectively identify patients with COVID-19 pneumonia. To achieve an efficient model, we combined three methods named: Convolution Neural Network (CNN), transfer learning, and the focal loss function which is used for imbalanced classes to build 3 binary classifiers, namely COVID-19 vs Normal, COVID-19 vs pneumonia and COVID-19 vs Normal Pneumonia (Normal and Pneumonia). A comparative study has been made between our proposed classifiers with well-known classifiers and provided enhanced results in terms of accuracy, specificity, sensitivity and precision. The high performance of this computer-Aided diagnostic technique may greatly increase the screening speed and reliability of COVID-19 detection. 2021 IEEE.
راعي المشروعThis work was supported by Qatar University Internal Grant IRCC-2020-001. The statements made herein are solely the responsibility of the authors.
اللغةen
الناشرIEEE
الموضوعchest X-ray images
convolutional neural network
COVID-19
focal loss function
العنوانMachine Learning Screening of COVID-19 Patients Based on X-ray Images for Imbalanced Classes
النوعConference Paper
رقم المجلد2021-June
dc.accessType Open Access


الملفات في هذه التسجيلة

Thumbnail

هذه التسجيلة تظهر في المجموعات التالية

عرض بسيط للتسجيلة