Per-COVID-19: A Benchmark Dataset for COVID-19 Percentage Estimation from CT-Scans

PDAtt-Unet: Pyramid Dual-Decoder Attention Unet for Covid-19 infection segmentation from CT-scans

Since the emergence of the Covid-19 pandemic in late 2019, medical imaging has been widely used to analyse this disease. Indeed, CT-scans of the lungs can help diagnose, detect, and quantify Covid-19 infection. In this paper, we address the segmentation of Covid-19 infection from CT-scans. To improve the performance of the Att-Unet architecture and maximize the use of the Attention Gate, we propose the PAtt-Unet and DAtt-Unet architectures. PAtt-Unet aims to exploit the input pyramids to preserve the spatial awareness in all of the encoder layers. On the other hand, DAtt-Unet is designed to guide the segmentation of Covid-19 infection inside the lung lobes. We also propose to combine these two architectures into a single one, which we refer to as PDAtt-Unet. To overcome the blurry boundary pixels segmentation of Covid-19 infection, we propose a hybrid loss function. The proposed architectures were tested on four datasets with two evaluation scenarios (intra and cross datasets). Experimental results showed that both PAtt-Unet and DAtt-Unet improve the performance of Att-Unet in segmenting Covid-19 infections. Moreover, the combination architecture PDAtt-Unet led to further improvement. To Compare with other methods, three baseline segmentation architectures (Unet, Unet++, and Att-Unet) and three state-of-the-art architectures (InfNet, SCOATNet, and nCoVSegNet) were tested. The comparison showed the superiority of the proposed PDAtt-Unet trained with the proposed hybrid loss (PDEAtt-Unet) over all other methods. Moreover, PDEAtt-Unet is able to overcome various challenges in segmenting Covid-19 infections in four datasets and two evaluation scenarios.

EffViT-COVID: A dual-path network for COVID-19 percentage estimation

The first case of novel Coronavirus (COVID-19) was reported in December 2019 in Wuhan City, China and led to an international outbreak. This virus causes serious respiratory illness and affects several other organs of the body differently for different patient. Worldwide, several waves of this infection have been reported, and researchers/doctors are working hard to develop novel solutions for the COVID diagnosis. Imaging and vision-based techniques are widely explored for the prediction of COVID-19; however, COVID infection percentage estimation is under explored. In this work, we propose a novel framework for the estimation of COVID-19 infection percentage based on deep learning techniques. The proposed network utilizes the features from vision transformers and CNN (Convolutional Neural Networks), specifically EfficientNet-B7. The features of both are fused together for preparing an information-rich feature vector that contributes to a more precise estimation of infection percentage. We evaluate our model on the Per-COVID-19 dataset (Bougourzi et al., 2021b) which comprises labeled CT data of COVID-19 patients. For the evaluation of the model on this dataset, we employ the most widely-used slice-level metrics, i.e., Pearson correlation coefficient (PC), Mean absolute error (MAE), and Root mean square error (RMSE). The network outperforms the other state-of-the-art methods and achieves 0 . 9886 ± 0 . 009 , 1 . 23 ± 0 . 378 , and 3 . 12 ± 1 . 56 , PC, MAE, and RMSE, respectively, using a 5-fold cross-validation technique. In addition, the overall average difference in the actual and predicted infection percentage is observed to be < 2 % . In conclusion, the detailed experimental results reveal the robustness and efficiency of the proposed network.

Comparison of Convolutional Neural Networks and Transformers for the Classification of Images of COVID-19, Pneumonia and Healthy Individuals as Observed with Computed Tomography

In this work, the performance of five deep learning architectures in classifying COVID-19 in a multi-class set-up is evaluated. The classifiers were built on pretrained ResNet-50, ResNet-50r (with kernel size 5×5 in the first convolutional layer), DenseNet-121, MobileNet-v3 and the state-of-the-art CaiT-24-XXS-224 (CaiT) transformer. The cross entropy and weighted cross entropy were minimised with Adam and AdamW. In total, 20 experiments were conducted with 10 repetitions and obtained the following metrics: accuracy (Acc), balanced accuracy (BA), F₁ and F₂ from the general Fβ macro score, Matthew’s Correlation Coefficient (MCC), sensitivity (Sens) and specificity (Spec) followed by bootstrapping. The performance of the classifiers was compared by using the Friedman–Nemenyi test. The results show that less complex architectures such as ResNet-50, ResNet-50r and DenseNet-121 were able to achieve better generalization with rankings of 1.53, 1.71 and 3.05 for the Matthew Correlation Coefficient, respectively, while MobileNet-v3 and CaiT obtained rankings of 3.72 and 5.0, respectively.

CNN based facial aesthetics analysis through dynamic robust losses and ensemble regression

In recent years, estimating beauty of faces has attracted growing interest in the fields of computer vision and machine learning. This is due to the emergence of face beauty datasets (such as SCUT-FBP, SCUT-FBP5500 and KDEF-PT) and the prevalence of deep learning methods in many tasks. The goal of this work is to leverage the advances in Deep Learning architectures to provide stable and accurate face beauty estimation from static face images. To this end, our proposed approach has three main contributions. To deal with the complicated high-level features associated with the FBP problem by using more than one pre-trained Convolutional Neural Network (CNN) model, we propose an architecture with two backbones (2B-IncRex). In addition to 2B-IncRex, we introduce a parabolic dynamic law to control the behavior of the robust loss parameters during training. These robust losses are ParamSmoothL1, Huber, and Tukey. As a third contribution, we propose an ensemble regression based on five regressors, namely Resnext-50, Inception-v3 and three regressors based on our proposed 2B-IncRex architecture. These models are trained with the following dynamic loss functions: Dynamic ParamSmoothL1, Dynamic Tukey, Dynamic ParamSmoothL1, Dynamic Huber, and Dynamic Tukey, respectively. To evaluate the performance of our approach, we used two datasets: SCUT-FBP5500 and KDEF-PT. The dataset SCUT-FBP5500 contains two evaluation scenarios provided by the database developers: 60-40% split and five-fold cross-validation. Our approach outperforms state-of-the-art methods on several metrics in both evaluation scenarios of SCUT-FBP5500. Moreover, experiments on the KDEF-PT dataset demonstrate the efficiency of our approach for estimating facial beauty using transfer learning, despite the presence of facial expressions and limited data. These comparisons highlight the effectiveness of the proposed solutions for FBP. They also show that the proposed Dynamic robust losses lead to more flexible and accurate estimators.

Determination of the Severity and Percentage of COVID-19 Infection through a Hierarchical Deep Learning System

The coronavirus disease 2019 (COVID-19) has caused millions of deaths and one of the greatest health crises of all time. In this disease, one of the most important aspects is the early detection of the infection to avoid the spread. In addition to this, it is essential to know how the disease progresses in patients, to improve patient care. This contribution presents a novel method based on a hierarchical intelligent system, that analyzes the application of deep learning models to detect and classify patients with COVID-19 using both X-ray and chest computed tomography (CT). The methodology was divided into three phases, the first being the detection of whether or not a patient suffers from COVID-19, the second step being the evaluation of the percentage of infection of this disease and the final phase is to classify the patients according to their severity. Stratification of patients suffering from COVID-19 according to their severity using automatic systems based on machine learning on medical images (especially X-ray and CT of the lungs) provides a powerful tool to help medical experts in decision making. In this article, a new contribution is made to a stratification system with three severity levels (mild, moderate and severe) using a novel histogram database (which defines how the infection is in the different CT slices for a patient suffering from COVID-19). The first two phases use CNN Densenet-161 pre-trained models, and the last uses SVM with LDA supervised learning algorithms as classification models. The initial stage detects the presence of COVID-19 through X-ray multi-class (COVID-19 vs. No-Findings vs. Pneumonia) and the results obtained for accuracy, precision, recall, and F1-score values are 88%, 91%, 87%, and 89%, respectively. The following stage manifested the percentage of COVID-19 infection in the slices of the CT-scans for a patient and the results in the metrics evaluation are 0.95 in Pearson Correlation coefficient, 5.14 in MAE and 8.47 in RMSE. The last stage finally classifies a patient in three degrees of severity as a function of global infection of the lungs and the results achieved are 95% accurate.