DSSAE: Deep Stacked Sparse Autoencoder Analytical Model for COVID-19 Diagnosis by Fractional Fourier Entropy

COVIDAL: A machine learning classifier for digital COVID-19 diagnosis in German hospitals

For the fight against the COVID-19 pandemic, it is particularly important to map the course of infection, in terms of patients who have currently tested SARS-CoV-2 positive, as accurately as possible. In hospitals, this is even more important because resources have become scarce. Although polymerase chain reaction (PCR) and point of care (POC) antigen testing capacities have been massively expanded, they are often very time-consuming and cost-intensive and, in some cases, lack appropriate performance. To meet these challenges, we propose the COVIDAL classifier for AI-based diagnosis of symptomatic COVID-19 subjects in hospitals based on laboratory parameters. We evaluate the algorithm's performance by unique multicenter data with approx. 4,000 patients and an extraordinary high ratio of SARS-CoV-2 positive patients. We analyze the influence of data preparation, flexibility in optimization targets as well as the selection of the test set on the COVIDAL outcome. The algorithm is compared with standard AI, PCR, POC antigen testing and manual classifications of seven physicians by a decision theoretic scoring model including performance metrics, turnaround times and cost. Thereby, we define health care settings in which a certain classifier for COVID-19 diagnosis is to be applied. We find sensitivities, specificities and accuracies of the COVIDAL algorithm of up to 90 percent. Our scoring model suggests using PCR testing for a focus on performance metrics. For turnaround times, POC antigen testing should be used. If balancing performance, turnaround times and cost is of interest, as, for example, in the emergency department, COVIDAL is superior based on the scoring model.

EVAE-Net: An Ensemble Variational Autoencoder Deep Learning Network for COVID-19 Classification Based on Chest X-ray Images

The COVID-19 pandemic has had a significant impact on many lives and the economies of many countries since late December 2019. Early detection with high accuracy is essential to help break the chain of transmission. Several radiological methodologies, such as CT scan and chest X-ray, have been employed in diagnosing and monitoring COVID-19 disease. Still, these methodologies are time-consuming and require trial and error. Machine learning techniques are currently being applied by several studies to deal with COVID-19. This study exploits the latent embeddings of variational autoencoders combined with ensemble techniques to propose three effective EVAE-Net models to detect COVID-19 disease. Two encoders are trained on chest X-ray images to generate two feature maps. The feature maps are concatenated and passed to either a combined or individual reparameterization phase to generate latent embeddings by sampling from a distribution. The latent embeddings are concatenated and passed to a classification head for classification. The COVID-19 Radiography Dataset from Kaggle is the source of chest X-ray images. The performances of the three models are evaluated. The proposed model shows satisfactory performance, with the best model achieving 99.19% and 98.66% accuracy on four classes and three classes, respectively.

Artificial neural networks for prediction of COVID-19 in India by using backpropagation

The COVID-19 pandemic has affected thousands of people around the world. In this study, we used artificial neural network (ANN) models to forecast the COVID-19 outbreak for policymakers based on 1st January to 31st October 2021 of positive cases in India. In the confirmed cases of COVID-19 in India, it's critical to use an estimating model with a high degree of accuracy to get a clear understanding of the situation. Two explicit mathematical prediction models were used in this work to anticipate the COVID-19 epidemic in India. A Boltzmann Function-based model and Beesham's prediction model are among these methods and also estimated using the advanced ANN-BP models. The COVID-19 information was partitioned into two sections: training and testing. The former was utilized for training the ANN-BP models, and the latter was used to test them. The information examination uncovers critical day-by-day affirmed case changes, yet additionally unmistakable scopes of absolute affirmed cases revealed across the time span considered. The ANN-BP model that takes into consideration the preceding 14-days outperforms the others based on the archived results. In forecasting the COVID-19 pandemic, this comparison provides the maximum incubation period, in India. Mean square error, and mean absolute percent error have been treated as the forecast model performs more accurately and gets good results. In view of the findings, the ANN-BP model that considers the past 14-days for the forecast is proposed to predict everyday affirmed cases, especially in India that have encountered the main pinnacle of the COVID-19 outbreak. This work has not just demonstrated the relevance of the ANN-BP techniques for the expectation of the COVID-19 outbreak yet additionally showed that considering the incubation time of COVID-19 in forecast models might produce more accurate assessments.

A lightweight CNN-based network on COVID-19 detection using X-ray and CT images

BACKGROUND AND OBJECTIVES

The traditional method of detecting COVID-19 disease mainly rely on the interpretation of computer tomography (CT) or X-ray images (X-ray) by doctors or professional researchers to identify whether it is COVID-19 disease, which is easy to cause identification mistakes. In this study, the technology of convolutional neural network is expected to be able to efficiently and accurately identify the COVID-19 disease.

METHODS

This study uses and fine-tunes seven convolutional neural networks including InceptionV3, ResNet50V2, Xception, DenseNet121, MobileNetV2, EfficientNet-B0, and EfficientNetV2 on COVID-19 detection. In addition, we proposes a lightweight convolutional neural network, LightEfficientNetV2, on small number of chest X-ray and CT images. Five-fold cross-validation was used to evaluate the performance of each model. To confirm the performance of the proposed model, LightEfficientNetV2 was carried out on three different datasets (NIH Chest X-rays, SARS-CoV-2 and COVID-CT).

RESULTS

On chest X-ray image dataset, the highest accuracy 96.50% was from InceptionV3 before fine-tuning; and the highest accuracy 97.73% was from EfficientNetV2 after fine-tuning. The accuracy of the LightEfficientNetV2 model proposed in this study is 98.33% on chest X-ray image. On CT images, the best transfer learning model before fine-tuning is MobileNetV2, with an accuracy of 94.46%; the best transfer learning model after fine-tuning is Xception, with an accuracy of 96.78%. The accuracy of the LightEfficientNetV2 model proposed in this study is 97.48% on CT image.

CONCLUSIONS

Compared with the SOTA, LightEfficientNetV2 proposed in this study demonstrates promising performance on chest X-ray images, CT images and three different datasets.

A facial expression recognizer using modified ResNet-152

In this age of artificial intelligence, facial expression recognition is an essential pool to describe emotion and psychology. In recent studies, many researchers have not achieved satisfactory results. This paper proposed an expression recognition system based on ResNet-152. Statistical analysis showed our method achieved 96.44% accuracy. Comparative experiments show that the model is better than mainstream models. In addition, we briefly described the application of facial expression recognition technology in the IoT (Internet of things).

Multi-Classification of Chest X-rays for COVID-19 Diagnosis Using Deep Learning Algorithms

Accurate detection of COVID-19 is of immense importance to help physicians intervene with appropriate treatments. Although RT-PCR is routinely used for COVID-19 detection, it is expensive, takes a long time, and is prone to inaccurate results. Currently, medical imaging-based detection systems have been explored as an alternative for more accurate diagnosis. In this work, we propose a multi-level diagnostic framework for the accurate detection of COVID-19 using X-ray scans based on transfer learning. The developed framework consists of three stages, beginning with a pre-processing step to remove noise effects and image resizing followed by a deep learning architecture utilizing an Xception pre-trained model for feature extraction from the pre-processed image. Our design utilizes a global average pooling (GAP) layer for avoiding over-fitting, and an activation layer is added in order to reduce the losses. Final classification is achieved using a softmax layer. The system is evaluated using different activation functions and thresholds with different optimizers. We used a benchmark dataset from the kaggle website. The proposed model has been evaluated on 7395 images that consist of 3 classes (COVID-19, normal and pneumonia). Additionally, we compared our framework with the traditional pre-trained deep learning models and with other literature studies. Our evaluation using various metrics showed that our framework achieved a high test accuracy of 99.3% with a minimum loss of 0.02 using the LeakyReLU activation function at a threshold equal to 0.1 with the RMSprop optimizer. Additionally, we achieved a sensitivity and specificity of 99 and F1-Score of 99.3% with only 10 epochs and a 10−4 learning rate.