Initial Stage Identification of COVID-19 Using Capsule Networks

Coronavirus is a pandemic that has kept us in great grief for the past few months. These days have created a devastating effect all through the world. As coronavirus has lot of similarities with other lung diseases, it becomes a challenging task for medical practitioners to identify the virus. A fast and robust system to identify the disease has been the need of the hour. In this chapter, we have used convolutional CapsNet for detecting COVID-19 disease using chest X-ray images. This design aims at obtaining fast and accurate diagnostic results. The proposed technique with less trainable parameters, COVID-CAPS, produced an accuracy of 87.5%, a sensitivity of 90%, a specificity of 95.8%, and an area under the curve (AUC) of 0.97. The main advantage of using CapsNet is that it can capture affine transformation in data that is a common scenario while dealing with real-world X-ray images. The CapsNet model is trained with normal data and tested with affine transformed data. The accuracy level obtained in the proposed method is comparatively much better along with having less learnable parameters and computational speed as compared to standard architectures such as ResNet, MobileNet, etc. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

DCACorrCapsNet: A deep channel‐attention correlative capsule network for COVID‐19 detection based on multi‐source medical images

The raging trend of COVID‐19 in the world has become more and more serious since 2019, causing large‐scale human deaths and affecting production and life. Generally speaking, the methods of detecting COVID‐19 mainly include the evaluation of human disease characterization, clinical examination and medical imaging. Among them, CT and X‐ray screening is conducive to doctors and patients' families to observe and diagnose the severity and development of the COVID‐19 more intuitively. Manual diagnosis of medical images leads to low the efficiency, and long‐term tired gaze will decline the diagnosis accuracy. Therefore, a fully automated method is needed to assist processing and analysing medical images. Deep learning methods can rapidly help differentiate COVID‐19 from other pneumonia‐related diseases or healthy subjects. However, due to the limited labelled images and the monotony of models and data, the learning results are biased, resulting in inaccurate auxiliary diagnosis. To address these issues, a hybrid model: deep channel‐attention correlative capsule network, for channel‐attention based spatial feature extraction, correlative feature extraction, and fused feature classification is proposed. Experiments are validated on X‐ray and CT image datasets, and the results outperform a large number of existing state‐of‐the‐art studies. [ABSTRACT FROM AUTHOR] Copyright of IET Image Processing (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Deep-CoV: An integrated deep learning model to detect COVID-19 using chest X-ray and CT images

The COVID-19 virus has fatal effect on lung function and due to its rapidity the early detection is necessary at the moment. The radiographic images have already been used by the researchers for the early diagnosis of COVID-19. Though several existing research exhibited very good performance with either x-ray or computer tomography (CT) images, to the best of our knowledge no such work has reported the assembled performance of both x-ray and CT images. Thus increase in accuracy with higher scalability is the main concern of the recent research. In this article, an integrated deep learning model has been developed for detection of COVID-19 at an early stage using both chest x-ray and CT images. The lack of publicly available data about COVID-19 disease motivates the authors to combine three benchmark datasets into a single dataset of large size. The proposed model has applied various transfer learning techniques for feature extraction and to find out the best suite. Finally the capsule network is used to categorize the sub-dataset into COVID positive and normal patients. The experimental results show that, the best performance exhibits by the ResNet50 with capsule network as an extractor-classifier pair with the combined dataset, which is composed of 575 numbers of x-ray images and 930 numbers of CT images. The proposed model achieves accuracy of 98.2% and 97.8% with x-ray and CT images, respectively, and an average of 98%. © 2023 Wiley Periodicals LLC.

Lung disease detection using Self-Attention Generative Adversarial Capsule network optimized with sun flower Optimization Algorithm

Lung cancer is the uncontrolled growth of abnormal cells in one or both lungs. This is one of the dangerous diseases. A lot of feature extraction with classification methods were discussed previously regarding this disease, but none of the methods give sufficient results, not only that, those methods have high over fitting problem, as a result, the detection accuracy was minimizing. Therefore, to overcome these issues, a Lung Disease Detection using Self-Attention Generative Adversarial Capsule Network optimized with Sun flower Optimization Algorithm (SA-Caps GAN-SFOA-LDC) is proposed in this manuscript. Initially, NIH chest X-ray image dataset is gathered through Kaggle repository to diagnose the lung disease. Then, the chests X-ray images are pre-processed by using the contrast limited adaptive histogram equalization (CLAHE) filtering method to eliminate the noise and to enhance the image quality. These pre-processed outputs are fed to feature extraction process. In the feature extraction process, the empirical wavelet transform method is used. These extracted features are given into Self-Attention based Generative Adversarial Capsule classifier for detecting the lung disease. The hyper parameters of SA-Caps GAN classifier is optimized using Sun flower Optimization Algorithm. The simulation is implemented in MATLAB. The proposed SA-Caps GAN-SFOA-LDC method attains higher accuracy 21.05%, 33.28%, 30.27%, 29.68%, 32.57% and 44.28%, Higher Precision 30.24%, 35.68%, 32.08%, 41.27%, 28.57% and 34.20%, Higher F-Score 32.05%, 31.05%, 36.24%, 30.27%, 37.59% and 22.05% analyzed with the existing methods, SVM-SMO-LDC, CNN-MOSHO-LDC, XGboost-PSO-LDC respectively. © 2022 Elsevier Ltd

Classification of COVID-19 from community-acquired pneumonia: Boosting the performance with capsule network and maximum intensity projection image of CT scans.

BACKGROUND: The coronavirus disease 2019 (COVID-19) and community-acquired pneumonia (CAP) present a high degree of similarity in chest computed tomography (CT) images. Therefore, a procedure for accurately and automatically distinguishing between them is crucial. METHODS: A deep learning method for distinguishing COVID-19 from CAP is developed using maximum intensity projection (MIP) images from CT scans. LinkNet is employed for lung segmentation of chest CT images. MIP images are produced by superposing the maximum gray of intrapulmonary CT values. The MIP images are input into a capsule network for patient-level pred iction and diagnosis of COVID-19. The network is trained using 333 CT scans (168 COVID-19/165 CAP) and validated on three external datasets containing 3581 CT scans (2110 COVID-19/1471 CAP). RESULTS: LinkNet achieves the highest Dice coefficient of 0.983 for lung segmentation. For the classification of COVID-19 and CAP, the capsule network with the DenseNet-121 feature extractor outperforms ResNet-50 and Inception-V3, achieving an accuracy of 0.970 on the training dataset. Without MIP or the capsule network, the accuracy decreases to 0.857 and 0.818, respectively. Accuracy scores of 0.961, 0.997, and 0.949 are achieved on the external validation datasets. The proposed method has higher or comparable sensitivity compared with ten state-of-the-art methods. CONCLUSIONS: The proposed method illustrates the feasibility of applying MIP images from CT scans to distinguish COVID-19 from CAP using capsule networks. MIP images provide conspicuous benefits when exploiting deep learning to detect COVID-19 lesions from CT scans and the capsule network improves COVID-19 diagnosis.

ACSN: Attention capsule sampling network for diagnosing COVID-19 based on chest CT scans.

Automated diagnostic techniques based on computed tomography (CT) scans of the chest for the coronavirus disease (COVID-19) help physicians detect suspected cases rapidly and precisely, which is critical in providing timely medical treatment and preventing the spread of epidemic outbreaks. Existing capsule networks have played a significant role in automatic COVID-19 detection systems based on small datasets. However, extracting key slices is difficult because CT scans typically show many scattered lesion sections. In addition, existing max pooling sampling methods cannot effectively fuse the features from multiple regions. Therefore, in this study, we propose an attention capsule sampling network (ACSN) to detect COVID-19 based on chest CT scans. A key slices enhancement method is used to obtain critical information from a large number of slices by applying attention enhancement to key slices. Then, the lost active and background features are retained by integrating two types of sampling. The results of experiments on an open dataset of 35,000 slices show that the proposed ACSN achieve high performance compared with state-of-the-art models and exhibits 96.3% accuracy, 98.8% sensitivity, 93.8% specificity, and 98.3% area under the receiver operating characteristic curve.

Competitive Capsule Network Based Sentiment Analysis on Twitter COVID'19 Vaccines

COVID-19 is an extremely contagious virus that has rapidly spread around the world. This disease has infected people of all ages in India, from children to the elderly. Vaccination, on the other hand, is the only way to preserve human lives. In the midst of a pandemic, it's critical to know what people think of COVID-19 immunizations. The primary goal of this article is to examine corona vaccination tweets from India's Twitter social media. This study introduces CompCapNets, a unique deep learning approach for Twitter sentiment classification. The results suggest that the proposed method outperforms other strategies when compared to existing traditional methods. © 2022 River Publishers.

An Ensemble of CapsNet and KSVM Using Feature Fusion: Application to COVID-19 Detection

COVID-19 virus is a major worldwide pandemic that is growing at a fast pace throughout the world. The usual approach for diagnosing COVID-19 is the use of a real-time polymerase chain reaction (RT-PCR) based nucleic acid test. However, RT-PCR has lower sensitivity in the early phases of COVID-19 detection. Recent studies have indicated that X-ray images may be useful throughout the early detection of the virus. Human screening has been shown to be cost-effective, susceptible to mistakes, and time-demanding, which has sparked an interest in using Convolutional Neural Networks (CNNs) to automate the process. CNNs, on the other hand, fail to view the exact placement of features as advantageous in medical imaging. Furthermore, for successful training and prediction, CNNs need a huge quantity of datasets. CNNs are rapidly reducing picture resolution, resulting in worsening accuracy in classification. We used newly created capsule networks (CapsNets) in our study to circumvent these disadvantages. The primary contribution is to improve the identification of SARS-CoV-2 with images obtained from X-ray by coupling capsule network with a kernel support vector machine (KSVM). The technique was evaluated using a publicly available dataset, and the proposed model shows that the accuracy of the CapsNet-KSVM based model is improved by 94.6% accuracy, 95% sensitivity, and 98% specificity, which outperforms the traditional CNN and other existing ensemble models. The proposed CapsNet-KSVM based system can be employed to identify the presence of COVID-19 in the human body using X-ray images. © 2022 IEEE.

Capsule Networks with Chest X-Ray Enhancement for Detection of COVID-19

Corona virus was declared a global pandemic that has affected people worldwide. It is critical to diagnose corona virus-infected individuals to restrict the virus's transmission. Recent research indicates that radiological methods provide valuable information in identifying infection using deep learning algorithms. Deep learning has contributed to large-scale medical data research, providing new ways and chances for diagnostic tools. This research attempted to investigate how the Capsule Networks leverage chest X-ray scans to identify the infected person. We suggest Capsule Networks identify the illness using chest X-ray data. The proposed approach is rapid and robust, classifying scans into COVID-19, No Findings, or any other issue in the lungs. The study can be used as a preliminary diagnosis by medical practitioners, and the study focuses on the COVID-19 class, a minority class in all public data sets accessible, and ensures that no COVID-19 infected individual is identified as Normal. Even with a small dataset, the model provides 96.37% accuracy for COVID-19 and for the non-COVID-19, and on multi-class classification, it provides an accuracy of 95.12%. © 2022 IEEE.

CapsCovNet: A Modified Capsule Network to Diagnose COVID-19 From Multimodal Medical Imaging.

Since the end of 2019, novel coronavirus disease (COVID-19) has brought about a plethora of unforeseen changes to the world as we know it. Despite our ceaseless fight against it, COVID-19 has claimed millions of lives, and the death toll exacerbated due to its extremely contagious and fast-spreading nature. To control the spread of this highly contagious disease, a rapid and accurate diagnosis can play a very crucial part. Motivated by this context, a parallelly concatenated convolutional block-based capsule network is proposed in this article as an efficient tool to diagnose the COVID-19 patients from multimodal medical images. Concatenation of deep convolutional blocks of different filter sizes allows us to integrate discriminative spatial features by simultaneously changing the receptive field and enhances the scalability of the model. Moreover, concatenation of capsule layers strengthens the model to learn more complex representation by presenting the information in a fine to coarser manner. The proposed model is evaluated on three benchmark datasets, in which two of them are chest radiograph datasets and the rest is an ultrasound imaging dataset. The architecture that we have proposed through extensive analysis and reasoning achieved outstanding performance in COVID-19 detection task, which signifies the potentiality of the proposed model.

Comparative Analysis of Deep Learning Techniques for Facemask Detection

Pandemic caused owing to widespread of corona-virus has changed our lives upside down. Covering the face area particularly nose and mouth is the prime need of the hour. Any negligence of not wearing the mask or incorrectly wearing the mask can be hazardous. This necessitates the need of understanding the real importance of wearing the mask appropriately in order to avoid the spread of Covid 19. Knowing the present population of the country, manual monitoring of the individuals is quite difficult. So, this research puts forward the use of deep learning techniques for automatic facemask detection using techniques such as capsule network, ResNet50, Mobile-Net architecture, and Convolution Neural Network. The techniques are validated on the merged dataset taken from MaskedFace-Net dataset and Kaggle (publicly available) based on the performance measures namely accuracy, precision, recall and F1-score. Amongst all, the results showed that capsule neural network achieved superlative performance with the accuracy of around 99% in comparison to other aforesaid deep learning techniques. © 2022, Springer Nature Switzerland AG.

Automated Diagnosis of Pneumonia through Capsule Network in conjunction with ResNet50v2 model

The world has witnessed one of the most devastating phases in the history of mankind after being hit with the COVID-19 pandemic which still continues to spread rapidly all across the globe. The disease is believed to majorly cause respiratory disorders in humans. Detecting COVID-19 patients through X-Ray images is the only way to slow down the expansion of the pandemic, detecting pneumonia has equally become a demanding task as both exhibit similar properties of affecting the human lungs. Pneumonia is said to be an illness caused by a bacteria in the alveoli of lungs that may accompany to the death of an individual if its treatment is ignored. Hence, developing an automated system to detect the disease can be beneficial to the human race. With continuous progressions in the expertise of deep learning and machine learning;its fundamentals are observed to continuously contribute towards analysis of medical images and classification of patients exhibiting the disease. In this work, we appraise the concepts of ResNet50v2 model and capsule network to predict the affected and unaffected patients using chest X-Ray images. The authors propose a novel classification framework consisting of a convolutional layer, primary capsule layer and digit capsule layer, wherein the radiographic images are categorized through dynamic routing followed by disease prediction through ResNet50v2 model. The proposed work is implemented on images with a resolution of $224 \times 224$ and a batch size of 10. Further, parametric functions are applied to verify the model being trained. © 2022 IEEE.

COVID-19 Identification Using Deep Capsule Network: A Perspective of Super-Resolution CNN on Low-Qality CXR Images

Chest X-ray has become a useful method in the detection of coronavirus disease-19 (COVID-19). Due to the extreme global COVID-19 crisis, using the computerized diagnosis method for COVID-19 classification upon CXR images could significantly decrease clinician workload. We explicitly addressed the issue of low CXR image resolution by using Super-Resolution Convolutional Neural Network (SRCNN) to effectively reconstruct high-resolution (HR) CXR images from low-resolution (LR) CXR correspondents. Then, the HRCXR images are fed into the modified capsule network to retrieve distinct features for the classification of COVID-19. We demonstrate the proposed model on a public dataset and achieve ACC of 97.3%, SEN of 97.8%, SPE of 96.9%, and AUC of 98.0%. This new conceptual framework is proposed to play a vital task in the issue facing COVID-19 and related ailments. © 2021 ACM.

CapsNet-COVID19: Lung CT image classification method based on CapsNet model.

The outbreak of the Corona Virus Disease 2019 (COVID-19) has posed a serious threat to human health and life around the world. As the number of COVID-19 cases continues to increase, many countries are facing problems such as errors in nucleic acid testing (RT-PCR), shortage of testing reagents, and lack of testing personnel. In order to solve such problems, it is necessary to propose a more accurate and efficient method as a supplement to the detection and diagnosis of COVID-19. This research uses a deep network model to classify some of the COVID-19, general pneumonia, and normal lung CT images in the 2019 Novel Coronavirus Information Database. The first level of the model uses convolutional neural networks to locate lung regions in lung CT images. The second level of the model uses the capsule network to classify and predict the segmented images. The accuracy of our method is 84.291% on the test set and 100% on the training set. Experiment shows that our classification method is suitable for medical image classification with complex background, low recognition rate, blurred boundaries and large image noise. We believe that this classification method is of great value for monitoring and controlling the growth of patients in COVID-19 infected areas.

A Deep Multi-kernel Uniform Capsule Approach for Hate Speech Detection

Hate Speech is an expression that expresses hatred towards people of a specific ethnic group or nationality and incites hatred. Even though many countries have anti-hate speech legislation, hate speech can spread in the native language on social media platforms, resulting in violent riots and protests that spiral out of control and result in anti-social events. Hence, hate speech has caused a crucial social issue. Thus, various intelligent mechanisms have been employed to classify hate speech, depending on the category. A deep learning model has certain limitations for providing n-gram features for text classification of the native language. As a result, in this paper, the Multi-kernel uniform capsule network for multilingual languages is proposed. The proposed method employs a Multi-kernel uniform capsule network to improve feature selection performance by utilizing the capsule network routing algorithm. The experiments were carried out on political, COVID-19 and vaccination, lockdown, and multilingual dataset. The experimental results demonstrate that the proposed methods achieve adequate results when compared with other machine learning models for hate speech detection. © 2022, Springer Nature Switzerland AG.

COVID-19 Pneumonia Classification Based on NeuroWavelet Capsule Network.

Since it was first reported, coronavirus disease 2019, also known as COVID-19, has spread expeditiously around the globe. COVID-19 must be diagnosed as soon as possible in order to control the disease and provide proper care to patients. The chest X-ray (CXR) has been identified as a useful diagnostic tool, but the disease outbreak has put a lot of pressure on radiologists to read the scans, which could give rise to fatigue-related misdiagnosis. Automatic classification algorithms that are reliable can be extremely beneficial; however, they typically depend upon a large amount of COVID-19 data for training, which are troublesome to obtain in the nick of time. Therefore, we propose a novel method for the classification of COVID-19. Concretely, a novel neurowavelet capsule network is proposed for COVID-19 classification. To be more precise, first, we introduce a multi-resolution analysis of a discrete wavelet transform to filter noisy and inconsistent information from the CXR data in order to improve the feature extraction robustness of the network. Secondly, the discrete wavelet transform of the multi-resolution analysis also performs a sub-sampling operation in order to minimize the loss of spatial details, thereby enhancing the overall classification performance. We examined the proposed model on a public-sourced dataset of pneumonia-related illnesses, including COVID-19 confirmed cases and healthy CXR images. The proposed method achieves an accuracy of 99.6%, sensitivity of 99.2%, specificity of 99.1% and precision of 99.7%. Our approach achieves an up-to-date performance that is useful for COVID-19 screening according to the experimental results. This latest paradigm will contribute significantly in the battle against COVID-19 and other diseases.

Active disease-related compound identification based on capsule network.

Pneumonia, especially corona virus disease 2019 (COVID-19), can lead to serious acute lung injury, acute respiratory distress syndrome, multiple organ failure and even death. Thus it is an urgent task for developing high-efficiency, low-toxicity and targeted drugs according to pathogenesis of coronavirus. In this paper, a novel disease-related compound identification model-based capsule network (CapsNet) is proposed. According to pneumonia-related keywords, the prescriptions and active components related to the pharmacological mechanism of disease are collected and extracted in order to construct training set. The features of each component are extracted as the input layer of capsule network. CapsNet is trained and utilized to identify the pneumonia-related compounds in Qingre Jiedu injection. The experiment results show that CapsNet can identify disease-related compounds more accurately than SVM, RF, gcForest and forgeNet.

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.

Convolutional capsule network for COVID-19 detection using radiography images.

Novel corona virus COVID-19 has spread rapidly all over the world. Due to increasing COVID-19 cases, there is a dearth of testing kits. Therefore, there is a severe need for an automatic recognition system as a solution to reduce the spreading of the COVID-19 virus. This work offers a decision support system based on the X-ray image to diagnose the presence of the COVID-19 virus. A deep learning-based computer-aided decision support system will be capable to differentiate between COVID-19 and pneumonia. Recently, convolutional neural network (CNN) is designed for the diagnosis of COVID-19 patients through chest radiography (or chest X-ray, CXR) images. However, due to the usage of CNN, there are some limitations with these decision support systems. These systems suffer with the problem of view-invariance and loss of information due to down-sampling. In this paper, the capsule network (CapsNet)-based system named visual geometry group capsule network (VGG-CapsNet) for the diagnosis of COVID-19 is proposed. Due to the usage of capsule network (CapsNet), the authors have succeeded in removing the drawbacks found in the CNN-based decision support system for the detection of COVID-19. Through simulation results, it is found that VGG-CapsNet has performed better than the CNN-CapsNet model for the diagnosis of COVID-19. The proposed VGG-CapsNet-based system has shown 97% accuracy for COVID-19 versus non-COVID-19 classification, and 92% accuracy for COVID-19 versus normal versus viral pneumonia classification. Proposed VGG-CapsNet-based system available at https://github.com/shamiktiwari/COVID19_Xray can be used to detect the existence of COVID-19 virus in the human body through chest radiographic images.

COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images.

Novel Coronavirus disease (COVID-19) has abruptly and undoubtedly changed the world as we know it at the end of the 2nd decade of the 21st century. COVID-19 is extremely contagious and quickly spreading globally making its early diagnosis of paramount importance. Early diagnosis of COVID-19 enables health care professionals and government authorities to break the chain of transition and flatten the epidemic curve. The common type of COVID-19 diagnosis test, however, requires specific equipment and has relatively low sensitivity. Computed tomography (CT) scans and X-ray images, on the other hand, reveal specific manifestations associated with this disease. Overlap with other lung infections makes human-centered diagnosis of COVID-19 challenging. Consequently, there has been an urgent surge of interest to develop Deep Neural Network (DNN)-based diagnosis solutions, mainly based on Convolutional Neural Networks (CNNs), to facilitate identification of positive COVID-19 cases. CNNs, however, are prone to lose spatial information between image instances and require large datasets. The paper presents an alternative modeling framework based on Capsule Networks, referred to as the COVID-CAPS, being capable of handling small datasets, which is of significant importance due to sudden and rapid emergence of COVID-19. Our results based on a dataset of X-ray images show that COVID-CAPS has advantage over previous CNN-based models. COVID-CAPS achieved an Accuracy of 95.7%, Sensitivity of 90%, Specificity of 95.8%, and Area Under the Curve (AUC) of 0.97, while having far less number of trainable parameters in comparison to its counterparts. To potentially and further improve diagnosis capabilities of the COVID-CAPS, pre-training and transfer learning are utilized based on a new dataset constructed from an external dataset of X-ray images. This is in contrary to existing works on COVID-19 detection where pre-training is performed based on natural images. Pre-training with a dataset of similar nature further improved accuracy to 98.3% and specificity to 98.6%.