COVID19XrayNet: A Two-Step Transfer Learning Model for the COVID-19 Detecting Problem Based on a Limited Number of Chest X-Ray Images

Abstract

Graphic abstract COVID19XrayNet, a two-step transfer learning framework designed for biomedical images.

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

“…Most of these papers used off-the-shelf networks, including ResNet-18 or ResNet-50 16,17,20,26,29,32,37 , DenseNet-121 27,28,31,32,34 , VGG-16 or VGG-19 19,33,35 , Inception 21,38 and EfficientNet 30,39 , with three considering custom architectures 18,25,36 and three using hand-engineered features [22][23][24] . Most papers classified images into the three classes, that is, COVID-19, non-COVID-19 pneumonia and normal 16,19,21,23,25,26,28,30,[32][33][34][35][36][37] , while two considered an extra class by dividing non-COVID-19 pneumonia into viral and bacterial pneumonia 17,29 . ResNet and DenseNet architectures showed better performance than the others, with accuracies ranging from 0.88 to 0.99.…”

“…Almost all papers had a high (45/62) or unclear (11/62) risk of bias for their participants, with only six assessed as having a low risk of bias. This was primarily due to the following issues: (1) for public datasets it is not possible to know whether patients are truly COVID-19 positive, or if they have underlying selection biases, as anybody can contribute images 16,24,26,28-32,34,35,37,41,44,48,49,76 ; (2) the paper uses only a subset of original datasets, applying some exclusion criteria, without enough details to be reproducible 16,43,44,48,49,51,61,70,71,75,76 ; and/or (3) there are large differences in demographics between the COVID-19 cohort and the control groups, with, for example, paediatric patients as controls 17,24,28,29,31,32,35,37,45,46,59,81 .…”

“…We see that 20/32 papers have a reasonable balance between classes (with exceptions being refs. 17,24,26,30,31,33,36,37,40,51,61,62 . However, the majority of datasets were quite small, with 19/32 papers using fewer than 2,000 datapoints for development (with exceptions being refs.…”

Accurately Discriminating COVID-19 from Viral and Bacterial Pneumonia According to CT Images Via Deep Learning

“…Li et al [ 12 ] used ResNet50 to discriminate COVID-19 from non-pneumonia or community-acquired pneumonia, acquiring a sensitivity of 90%. Then, Chen et al [ 13 ], Zheng et al [ 14 ], Jin et al [ 15 ], Wang et al [ 16 ], Shi et al [ 17 ], Rasheed et al [ 18 ], Zhang et al [ 19 ], Ouyang et al [ 20 ], Han et al [ 21 ], Kang et al [ 22 ], Apostolopoulos et al [ 23 ] and Jaiswal et al [ 24 ] also aimed to separate COVID-19 infected patients from nonCOVID-19 subjects and other pneumonia. However, all of these works have ignored typical viral pneumonia that is infected by typical virus, which is also the most challenging as COVID-19 is also a kind of virus.…”

Insight into an unsupervised two-step sparse transfer learning algorithm for speech diagnosis of Parkinson’s disease

“…The two-step transfer learning methods have achieved significant results in some areas recently [41][42][43]. For instance, Sakurai et al [41] achieved semantic plant segmentation by two-step domain adaptation: firstly, adaptation is from a large amount of labeled data to a major category and then adapted category adaptation from the major category to a minor category; An et al [42] realized age-related macular degeneration diagnosis based on twice transfer of models: firstly, a pre-trained VGG16 model was used, and then, the fine-tuned model in the first step was to transfer learned again to distinguish the images; Similar to G, Zhang et al [43] utilized two-step transfer learning to detect COVID-19 based on model-transfer, but in different models. Specifically, all the above two-step transfer approaches are based on images and designed differently for different data characteristics and tasks, but are not considered in PD speech recognition.…”