Deep periocular representation aiming video surveillance

Luz, Eduardo; Moreira, Gladston; Zanlorensi, Luiz A.; Menotti, David

doi:10.1016/j.patrec.2017.12.009

“…In [ 18 ], four new blocks are added to the baseline model to improve COVID-19 recognition on x-ray images. Here, we proposed modifications aimed at CT images, and six new blocks are added to an EfficientNet B0 architecture.…”

Section: Methodsologymentioning

confidence: 99%

COVID-19 detection in CT images with deep learning: A voting-based scheme and cross-datasets analysis

Silva

¹

,

Luz

²

,

Silva

³

et al. 2020

Informatics in Medicine Unlocked

Self Cite

View full text Add to dashboard Cite

Early detection and diagnosis are critical factors to control the COVID-19 spreading. A number of deep learning-based methodologies have been recently proposed for COVID-19 screening in CT scans as a tool to automate and help with the diagnosis. These approaches, however, suffer from at least one of the following problems: (i) they treat each CT scan slice independently and (ii) the methods are trained and tested with sets of images from the same dataset. Treating the slices independently means that the same patient may appear in the training and test sets at the same time which may produce misleading results. It also raises the question of whether the scans from the same patient should be evaluated as a group or not. Moreover, using a single dataset raises concerns about the generalization of the methods. Different datasets tend to present images of varying quality which may come from different types of CT machines reflecting the conditions of the countries and cities from where they come from. In order to address these two problems, in this work, we propose an Efficient Deep Learning Technique for the screening of COVID-19 with a voting-based approach. In this approach, the images from a given patient are classified as group in a voting system. The approach is tested in the two biggest datasets of COVID-19 CT analysis with a patient-based split. A cross dataset study is also presented to assess the robustness of the models in a more realistic scenario in which data comes from different distributions. The cross-dataset analysis has shown that the generalization power of deep learning models is far from acceptable for the task since accuracy drops from 87.68% to 56.16% on the best evaluation scenario. These results highlighted that the methods that aim at COVID-19 detection in CT-images have to improve significantly to be considered as a clinical option and larger and more diverse datasets are needed to evaluate the methods in a realistic scenario.

show abstract

“…The batch normalization operation constrains the output of the layer in a speci c range, forcing zero mean and standard deviation one. That works as a regularization, increasing the stability of the neural network, and accelerating the training [19].…”

Section: E Cientcovidnetmentioning

confidence: 99%

Efficient Deep Learning Model for COVID-19 Detection in large CT images datasets: A cross-dataset analysis

Luz

¹

,

Silva

²

,

Moreira

³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Early detection and diagnosis are critical factors to control the COVID-19 spreading. A number of deep learning-based methodologies have been recently proposed for COVID-19 screening in CT scans as a tool to automate and help with the diagnosis. To achieve these goals, in this work, we propose a slice voting-based approach extending the EfficientNet Family of deep artificial neural networks.We also design a specific data augmentation process and transfer learning for such task.Moreover, a cross-dataset study is performed into the two largest datasets to date. The proposed method presents comparable results to the state-of-the-art methods and the highest accuracy to date on both datasets (accuracy of 87.60\% for the COVID-CT dataset and accuracy of 98.99% for the SARS-CoV-2 CT-scan dataset). The cross-dataset analysis showed that the generalization power of deep learning models is far from acceptable for the task since accuracy drops from 87.68% to 56.16% on the best evaluation scenario.These results highlighted that the methods that aim at COVID-19 detection in CT-images have to improve significantly to be considered as a clinical option and larger and more diverse datasets are needed to evaluate the methods in a realistic scenario.

show abstract

“…In (Luz et al, 2018), as shown in Table 2, the authors add four new blocks to the baseline model to improve the EfficientNet performance on the COVID-19 screening problem. Here, this model will be called EfficientNet-C19 B0.…”

Section: Efficientnet-c19mentioning

confidence: 99%

Pictures of X-Rays Displayed in Monitors for Deep Learning-Based COVID-19 Screening: Implications for Mobile Application Development

Luz

¹

,

Moreira

²

,

Gomes

³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

As the world faces the COVID-19 pandemic, Artificial Intelligence, in particular, Deep Learning (DL) have been called up for help. Several recent research papers have shown the usefulness of these techniques for COVID-19 screening in Chest X-Rays (CXRs). To make this technology accessible and easy to use for the healthcare workers a natural path is to embed it into a mobile app. In these cases, however, the DL models must be prepared to receive as inputs pictures taken with the smartphones.Trying to raise awareness about the limitations of these models in a real-world setup, in this work, a dataset of CXR pictures taken of computer monitors with smartphones is built and DL models are evaluated on it. The results show that the current models are not able to correctly classify this kind of input. In the tested setup, augmenting the dataset with such pictures has shown to mitigate the problem, but it was not enough to raise accuracy to acceptable levels. As an alternative, this work shows that it is possible to build a model that discards pictures of monitors such that the COVID-19 screening module does not have to cope with them.

show abstract

Deep periocular representation aiming video surveillance

Cited by 35 publications

References 27 publications

COVID-19 detection in CT images with deep learning: A voting-based scheme and cross-datasets analysis

COVID-19 detection in CT images with deep learning: A voting-based scheme and cross-datasets analysis

Efficient Deep Learning Model for COVID-19 Detection in large CT images datasets: A cross-dataset analysis

Pictures of X-Rays Displayed in Monitors for Deep Learning-Based COVID-19 Screening: Implications for Mobile Application Development

Contact Info

Product

Resources

About