Deep Learning for Automated Recognition of Covid-19 from Chest X-ray Images

Abstract

Background: The pandemic caused by coronavirus in recent months is having a devastating global effect, which puts the world under the most ever unprecedented emergency. Currently, since there are not effective antiviral treat- ments for Covid-19 yet, it is crucial to early detect and monitor the progression of the disease, thus helping to reduce mortality. While a corresponding vaccine is being developed, and different measures are being used to combat the virus, medical imaging techniques have also been investigated to assist doctors in diag- nosing this disease. Objective: This paper presents a practical solution for the detection of Covid-19 from chest X-ray (CXR) images, exploiting cutting-edge Machine Learning techniques. Methods: We employ EfficientNet and MixNet, two recently developed families of deep neural networks, as the main classifica- tion engine. Furthermore, we also apply different transfer learning strategies, aiming at making the training process more accurate and efficient. The proposed approach has been validated by means of two real datasets, the former consists of 13,511 training images and 1,489 testing images, the latter has 14,324 and 3,581 images for training and testing, respectively. Results: The results are promising: by all the experimental configurations considered in the evaluation,our approach always yields an accuracy larger than 95.0%, with the maximum accuracy obtained being 96.64%. Conclusions: As a comparison with various existing studies, we can thus conclude that our performance improvement is significant.

DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic

“…The feature extractor tends to encode model inputs by representing specific features that help it to learn and discover the relevant patterns related to the query object(s). Examples of feature extraction architectures can be seen in VGG16 46 , ResNet-50 47 , CSPResNeXt-50 48 , CSPDarknet53 48 , and EfficientNet-B0/B7 49 . The head of a DNN is responsible for the classifying the objects (e.g.…”

DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic

“…The feature extractor tends to encode model inputs by representing specific features that help it to learn and discover the relevant patterns related to the query object(s). Examples of feature extraction architectures can be seen in VGG16 46 , ResNet-50 47 , CSPResNeXt-50 48 , CSPDarknet53 48 , and EfficientNet-B0/B7 49 . The head of a DNN is responsible for classifying the objects (e.g.…”

Data augmentation approaches using cycle-consistent adversarial networks for improving COVID-19 screening in portable chest X-ray images

“…Generally, given the relevance of the COVID-19 disease, we can find some contributions from the state of the art to solve several tasks. As reference, these works perform useful tasks in the application field as lung segmentation ( Teixeira et al, 2021 , Vidal et al, 2021 ), screening and classification ( Abbas et al, 2020 , Asif et al, 2020 , Basu et al, 2020 , Duong et al, 2020 , Ismael and engür, 2021 , Misra et al, 2020 , de Moura et al, 2020 , Ozturk et al, 2020 , Sharma et al, 2020 , Yeh et al, 2020 ). These works are characterized for using deep learning strategies to distinguish among COVID-19 and other scenarios (healthy or pathological), providing satisfactory results.…”