Author: Okolo, Gabriel Iluebe; Katsigiannis, Stamos; Althobaiti, Turke; Ramzan, Naeem
Title: On the Use of Deep Learning for Imaging-Based COVID-19 Detection Using Chest X-rays Cord-id: ayv65yjd Document date: 2021_8_24
ID: ayv65yjd
Snippet: The global COVID-19 pandemic that started in 2019 and created major disruptions around the world demonstrated the imperative need for quick, inexpensive, accessible and reliable diagnostic methods that would allow the detection of infected individuals with minimal resources. Radiography, and more specifically, chest radiography, is a relatively inexpensive medical imaging modality that can potentially offer a solution for the diagnosis of COVID-19 cases. In this work, we examined eleven deep con
Document: The global COVID-19 pandemic that started in 2019 and created major disruptions around the world demonstrated the imperative need for quick, inexpensive, accessible and reliable diagnostic methods that would allow the detection of infected individuals with minimal resources. Radiography, and more specifically, chest radiography, is a relatively inexpensive medical imaging modality that can potentially offer a solution for the diagnosis of COVID-19 cases. In this work, we examined eleven deep convolutional neural network architectures for the task of classifying chest X-ray images as belonging to healthy individuals, individuals with COVID-19 or individuals with viral pneumonia. All the examined networks are established architectures that have been proven to be efficient in image classification tasks, and we evaluated three different adjustments to modify the architectures for the task at hand by expanding them with additional layers. The proposed approaches were evaluated for all the examined architectures on a dataset with real chest X-ray images, reaching the highest classification accuracy of 98.04% and the highest F1-score of 98.22% for the best-performing setting.
Search related documents:
Co phrase search for related documents- accessible easily rt pcr and accuracy specificity sensitivity: 1
- accessible easily rt pcr and machine learning: 1
- accessible protect and machine learning: 1
- accessible relatively inexpensive and acute respiratory syndrome: 1
- accuracy achieve and acr radiology american college: 1
- accuracy achieve and activation function: 1
- accuracy achieve and acute respiratory syndrome: 1, 2, 3, 4, 5, 6
- accuracy achieve and loss function: 1, 2
- accuracy achieve and lung region: 1
- accuracy achieve and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36
- accuracy achieve and machine learning model: 1, 2, 3, 4
- accuracy improve and activation function: 1
- accuracy improve and acute respiratory syndrome: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38
- accuracy improve and loss function: 1, 2, 3, 4
Co phrase search for related documents, hyperlinks ordered by date