Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning

By Shervin Minaee, R. Kafieh, Milan Sonka, Shakib Yazdani, Ghazaleh Jamalipour Soufi. Medical Image Anal., 2020

891 citations recorded by the source corpus.

Highlights • Preparing a dataset of around 5000 X-ray images for COVID-19 detection.• Training 4 state-of-the-art convolutional networks for COVID-19 detection.• Presenting the sensitivity, specificity, ROC curve, AOC, and confusion matrix for each model.• Achieving sensitivity and specificity rate of higher than 90% with high confidence interval.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/b9576ea1afef9ef16af4ac1dae6ea29468109225

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Radiology, Nuclear Medicine and Imaging

In Sociology and Political Science

Citations in the corpus

COVID-19 Image Data Collection: Prospective Predictions Are the Future (2020, methodology): “…similar to what we use for baselines in this work, where a model that is pretrained on existing CXR datasets is used to construct features from the images in this dataset which are lower dimensional and are less prone to overﬁtting [Apostolopoulos and Mpesiana, 2020] [Minaee et al., 2020].”
Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images (2021, positive): “[7], and can be useful in COVID-19 detection as well [8,9].”
Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model (2021, positive): “Since the time series used in this study are also nonlinear and irregular, so it can be said that the results of this study are consistent with previous studies.”
A review on deep learning in medical image analysis (2021, methodology): “Decision trees and support vector machines are two examples of it [29, 30].”
Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices (2020, background): “Deep learning techniques (Resnet50 and VGG16) [22] X-ray 102 both positive COVID-19 cases [18], [24], [25] and pneumonia cases [26].”
Evaluation of artificial intelligence techniques in disease diagnosis and prediction (2023, background): “Prompt action based on accurate data has a significant social and financial impact on the lives of people around the world [51].”
Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda (2022, background): “Trends, such as the charge for putting away and directing realities, information collection through electronic well-being records (Minaee et al. 2020; Kumar 2020), and exponential client state of information, have made a data-rich medical care biological system.”