Recognition of COVID-19 from CT Scans Using Two-Stage Deep-Learning-Based Approach: CNR-IEMN

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.

Omnidirectional 2.5D representation for COVID-19 diagnosis using chest CTs

The Coronavirus Disease 2019 (COVID-19) has drastically overwhelmed most countries in the last two years, and image-based approaches using computerized tomography (CT) have been used to identify pulmonary infections. Recent methods based on deep learning either require time-consuming per-slice annotations (2D) or are highly data- and hardware-demanding (3D). This work proposes a novel omnidirectional 2.5D representation of volumetric chest CTs that allows exploring efficient 2D deep learning architectures while requiring volume-level annotations only. Our learning approach uses a siamese feature extraction backbone applied to each lung. It combines these features into a classification head that explores a novel combination of Squeeze-and-Excite strategies with Class Activation Maps. We experimented with public and in-house datasets and compared our results with state-of-the-art techniques. Our analyses show that our method provides better or comparable prediction quality and accurately distinguishes COVID-19 infections from other kinds of pneumonia and healthy lungs.

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.

Efficient and visualizable convolutional neural networks for COVID-19 classification using Chest CT

With coronavirus disease 2019 (COVID-19) cases rising rapidly, deep learning has emerged as a promising diagnosis technique. However, identifying the most accurate models to characterize COVID-19 patients is challenging because comparing results obtained with different types of data and acquisition processes is non-trivial. In this paper we designed, evaluated, and compared the performance of 20 convolutional neutral networks in classifying patients as COVID-19 positive, healthy, or suffering from other pulmonary lung infections based on chest computed tomography (CT) scans, serving as the first to consider the EfficientNet family for COVID-19 diagnosis and employ intermediate activation maps for visualizing model performance. All models are trained and evaluated in Python using 4173 chest CT images from the dataset entitled "A COVID multiclass dataset of CT scans," with 2168, 758, and 1247 images of patients that are COVID-19 positive, healthy, or suffering from other pulmonary infections, respectively. EfficientNet-B5 was identified as the best model with an F1 score of 0.9769 ± 0.0046, accuracy of 0.9759 ± 0.0048, sensitivity of 0.9788 ± 0.0055, specificity of 0.9730 ± 0.0057, and precision of 0.9751 ± 0.0051. On an alternate 2-class dataset, EfficientNetB5 obtained an accuracy of 0.9845 ± 0.0109, F1 score of 0.9599 ± 0.0251, sensitivity of 0.9682 ± 0.0099, specificity of 0.9883 ± 0.0150, and precision of 0.9526 ± 0.0523. Intermediate activation maps and Gradient-weighted Class Activation Mappings offered human-interpretable evidence of the model's perception of ground-class opacities and consolidations, hinting towards a promising use-case of artificial intelligence-assisted radiology tools. With a prediction speed of under 0.1 s on GPUs and 0.5 s on CPUs, our proposed model offers a rapid, scalable, and accurate diagnostic for COVID-19.

A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain

With the global spread of the COVID-19 epidemic, a reliable method is required for identifying COVID-19 victims. The biggest issue in detecting the virus is a lack of testing kits that are both reliable and affordable. Due to the virus's rapid dissemination, medical professionals have trouble finding positive patients. However, the next real-life issue is sharing data with hospitals around the world while considering the organizations' privacy concerns. The primary worries for training a global Deep Learning (DL) model are creating a collaborative platform and personal confidentiality. Another challenge is exchanging data with health care institutions while protecting the organizations' confidentiality. The primary concerns for training a universal DL model are creating a collaborative platform and preserving privacy. This paper provides a model that receives a small quantity of data from various sources, like organizations or sections of hospitals, and trains a global DL model utilizing blockchain-based Convolutional Neural Networks (CNNs). In addition, we use the Transfer Learning (TL) technique to initialize layers rather than initialize randomly and discover which layers should be removed before selection. Besides, the blockchain system verifies the data, and the DL method trains the model globally while keeping the institution's confidentiality. Furthermore, we gather the actual and novel COVID-19 patients. Finally, we run extensive experiments utilizing Python and its libraries, such as Scikit-Learn and TensorFlow, to assess the proposed method. We evaluated works using five different datasets, including Boukan Dr. Shahid Gholipour hospital, Tabriz Emam Reza hospital, Mahabad Emam Khomeini hospital, Maragheh Dr.Beheshti hospital, and Miandoab Abbasi hospital datasets, and our technique outperform state-of-the-art methods on average in terms of precision (2.7%), recall (3.1%), F1 (2.9%), and accuracy (2.8%).

Fully automatic pipeline of convolutional neural networks and capsule networks to distinguish COVID-19 from community-acquired pneumonia via CT images

Background

Chest computed tomography (CT) is crucial in the diagnosis of coronavirus disease 2019 (COVID-19). However, the persistent pandemic and similar CT manifestations between COVID-19 and community-acquired pneumonia (CAP) raise methodological requirements.

Methods

A fully automatic pipeline of deep learning is proposed for distinguishing COVID-19 from CAP using CT images. Inspired by the diagnostic process of radiologists, the pipeline comprises four connected modules for lung segmentation, selection of slices with lesions, slice-level prediction, and patient-level prediction. The roles of the first and second modules and the effectiveness of the capsule network for slice-level prediction were investigated. A dataset of 326 CT scans was collected to train and test the pipeline. Another public dataset of 110 patients was used to evaluate the generalization capability.

Results

LinkNet exhibited the largest intersection over union (0.967) and Dice coefficient (0.983) for lung segmentation. For the selection of slices with lesions, the capsule network with the ResNet50 block achieved an accuracy of 92.5% and an area under the curve (AUC) of 0.933. The capsule network using the DenseNet121 block demonstrated better performance for slice-level prediction, with an accuracy of 97.1% and AUC of 0.992. For both datasets, the prediction accuracy of our pipeline was 100% at the patient level.

Conclusions

The proposed fully automatic deep learning pipeline of deep learning can distinguish COVID-19 from CAP via CT images rapidly and accurately, thereby accelerating diagnosis and augmenting the performance of radiologists. This pipeline is convenient for use by radiologists and provides explainable predictions.

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

COVID-19 infection recognition is a very important step in the fight against the COVID-19 pandemic. In fact, many methods have been used to recognize COVID-19 infection including Reverse Transcription Polymerase Chain Reaction (RT-PCR), X-ray scan, and Computed Tomography scan (CT- scan). In addition to the recognition of the COVID-19 infection, CT scans can provide more important information about the evolution of this disease and its severity. With the extensive number of COVID-19 infections, estimating the COVID-19 percentage can help the intensive care to free up the resuscitation beds for the critical cases and follow other protocol for less severity cases. In this paper, we introduce COVID-19 percentage estimation dataset from CT-scans, where the labeling process was accomplished by two expert radiologists. Moreover, we evaluate the performance of three Convolutional Neural Network (CNN) architectures: ResneXt-50, Densenet-161, and Inception-v3. For the three CNN architectures, we use two loss functions: MSE and Dynamic Huber. In addition, two pretrained scenarios are investigated (ImageNet pretrained models and pretrained models using X-ray data). The evaluated approaches achieved promising results on the estimation of COVID-19 infection. Inception-v3 using Dynamic Huber loss function and pretrained models using X-ray data achieved the best performance for slice-level results: 0.9365, 5.10, and 9.25 for Pearson Correlation coefficient (PC), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE), respectively. On the other hand, the same approach achieved 0.9603, 4.01, and 6.79 for PCsubj, MAEsubj, and RMSEsubj, respectively, for subject-level results. These results prove that using CNN architectures can provide accurate and fast solution to estimate the COVID-19 infection percentage for monitoring the evolution of the patient state.