Identification of COVID-19 in CT Images using Deep Learning Model

Worldwide, COVID-19 has had a substantial impact on patients and hospital systems. Early identification and diagnosis are essential for regulating the growth of COVID-19. The input CT screening images are initially segmented into various regions using the Fuzzy C-means (FCM) clustering technique. Next, region-based image quality enhancement employs a histogram equalization method. Furthermore, certain necessary data is represented in a new image using the Local Directional Number technique. Lastly, the input images are portioned with the help of a traditional convolutional neural network model. The proposed convolutional neural network based system was able to give an accuracy of 98.60%, and the results revealed that methods for detecting COVID-19 impact from CT scan images must be developed significantly before considering it as a medical choice. Moreover, many diverse datasets are essential to assess the processes in a real-world setting. © 2022 IEEE.

Hybrid Particle Swarm Optimized and Fuzzy C Means Clustering based segmentation technique for investigation of COVID-19 infected chest CT

COVID-19 is the world's most serious threat, affecting billions of people worldwide. Medical imaging, such as CT, has a lot of potential as an alternative to RT-PCR approach for significant judgement and disease control. As a result, automatic image segmentation is in high demand as a therapeutic decision aid. According to studies, medical images may be very useful for early screening since certain aspects of the image imply the existence of virus of COVID-19 and hence may be used as an efficient scanning tool. The proposed work presents a hybrid approach for efficient screening of COVID-19 using chest CT images implementing Hybrid Particle Swarm Optimised-Fuzzy C Means Clustering. The proposed method is tested on 15 chest CT images of COVID-19-infected patients and the results have been validated quantitatively by metrices such as entropy, contrast and standard deviation, which clearly outperforms the conventional Fuzzy C Means Clustering.Copyright © 2022 Informa UK Limited, trading as Taylor & Francis Group.

Forecasting Trend of Coronavirus Disease 2019 using Multi-Task Weighted TSK Fuzzy System

Artificial intelligence-(AI) based fog/edge computing has become a promising paradigm for infectious disease. Various AI algorithms are embedded in cooperative fog/edge devices to construct medical Internet of Things environments, infectious disease forecast systems, smart health, and so on. However, these systems are usually done in isolation, which is called single-task learning. They do not consider the correlation and relationship between multiple/different tasks, so some common information in the model parameters or data characteristics is lost. In this study, each data center in fog/edge computing is considered as a task in the multi-task learning framework. In such a learning framework, a multi-task weighted Takagi-Sugeno-Kang (TSK) fuzzy system, called MW-TSKFS, is developed to forecast the trend of Coronavirus disease 2019 (COVID-19). MW-TSKFS provides a multi-task learning strategy for both antecedent and consequent parameters of fuzzy rules. First, a multi-task weighted fuzzy c-means clustering algorithm is developed for antecedent parameter learning, which extracts the public information among all tasks and the private information of each task. By sharing the public cluster centroid and public membership matrix, the differences of commonality and individuality can be further exploited. For consequent parameter learning of MW-TSKFS, a multi-task collaborative learning mechanism is developed based on ϵ-insensitive criterion and L2 norm penalty term, which can enhance the generalization and forecasting ability of the proposed fuzzy system. The experimental results on the real COVID-19 time series show that the forecasting tend model based on multi-task the weighted TSK fuzzy system has a high application value. © 2021 Association for Computing Machinery.

COVID-19 ground-glass opacity segmentation based on fuzzy c-means clustering and improved random walk algorithm.

Accurate segmentation of ground-glass opacity (GGO) is an important premise for doctors to judge COVID-19. Aiming at the problem of mis-segmentation for GGO segmentation methods, especially the problem of adhesive GGO connected with chest wall or blood vessel, this paper proposes an accurate segmentation of GGO based on fuzzy c-means (FCM) clustering and improved random walk algorithm. The innovation of this paper is to construct a Markov random field (MRF) with adaptive spatial information by using the spatial gravity Model and the spatial structural characteristics, which is introduced into the FCM model to automatically balance the insensitivity to noise and preserve the effectiveness of image edge details to improve the clustering accuracy of image. Then, the coordinate values of nodes and seed points in the image are combined with the spatial distance, and the geodesic distance is added to redefine the weight. According to the edge density of the image, the weight of the grayscale and the spatial feature in the weight function is adaptively calculated. In order to reduce the influence of edge noise on GGO segmentation, an adaptive snowfall model is proposed to preprocess the image, which can suppress the noise without losing the edge information. In this paper, CT images of different types of COVID-19 are selected for segmentation experiments, and the experimental results are compared with the traditional segmentation methods and several SOTA methods. The results suggest that the paper method can be used for the auxiliary diagnosis of COVID-19, so as to improve the work efficiency of doctors.

An integrated understanding of the evolutionary and structural features of the SARS-CoV-2 spike receptor binding domain (RBD).

Conventional drug development strategies typically use pocket in protein structures as drug-target sites. They overlook the plausible effects of protein evolvability and resistant mutations on protein structure which in turn may impair protein-drug interaction. In this study, we used an integrated evolution and structure guided strategy to develop potential evolutionary-escape resistant therapeutics using receptor binding domain (RBD) of SARS-CoV-2 spike-protein/S-protein as a model. Deploying an ensemble of sequence space exploratory tools including co-evolutionary analysis and deep mutational scans we provide a quantitative insight into the evolutionarily constrained subspace of the RBD sequence-space. Guided by molecular simulation and structure network analysis we highlight regions inside the RBD, which are critical for providing structural integrity and conformational flexibility. Using fuzzy C-means clustering we combined evolutionary and structural features of RBD and identified a critical region. Subsequently, we used computational drug screening using a library of 1615 small molecules and identified one lead molecule, which is expected to target the identified region, critical for evolvability and structural stability of RBD. This integrated evolution-structure guided strategy to develop evolutionary-escape resistant lead molecules have potential general applications beyond SARS-CoV-2.

Pythagorean fuzzy time series model based on Pythagorean fuzzy c-means and improved Markov weighted in the prediction of the new COVID-19 cases.

Time series is an extremely important branch of prediction, and the research on it plays an important guiding role in production and life. To get more realistic prediction results, scholars have explored the combination of fuzzy theory and time series. Although some results have been achieved so far, there are still gaps in the combination of n-Pythagorean fuzzy sets and time series. In this paper, a pioneering n-Pythagorean fuzzy time series model (n-PFTS) and its forecasting method (n-IMWPFCM) are proposed to employ a n-Pythagorean fuzzy c-means clustering method (n-PFCM) to overcome the subjectivity of directly assigning membership and non-membership values, thus improving the accuracy of the partition the universe of discourse. A novel improved Markov prediction method is exploited to enhance the prediction accuracy of the model. The proposed prediction method is applied to the yearly University of Alabama enrollments data and the new COVID-19 cases data. The results show that compared with the traditional fuzzy time series forecasting method, the proposed method has better forecasting accuracy. Meanwhile, it has the characteristics of low computational complexity and high interpretability and demonstrates the superiority of this model from a realistic perspective.

Distribution of COVID-19 cases and deaths in Europe during the first 12 peak weeks of outbreak.

OBJECTIVE: The aim of the study was to identify similar WHO European countries in COVID-19 incidence and mortality rate during the first 12 peak weeks of pandemic outbreak to find out whether exact coherent parts of Europe were more affected than others, and to set relationship between age and higher COVID-19 mortality rate. METHODS: COVID-19 cases and deaths from 28 February to 21 May 2020 of 37 WHO European countries were aggregated into 12 consecutive weeks. The fuzzy C-means clustering was performed to identify similar countries in COVID-19 incidence and mortality rate. Pearson product-moment correlation coefficient and log-log linear regression analyses were performed to set up relation between COVID-19 mortality rate and age. Mann-Whitney (Wilcoxon) test was used to explore differences between countries possessing higher mortality rate and age. RESULTS: Based on the highest value of the coefficient of overall separation five clusters of similar countries were identified for incidence rate, mortality rate and in total. Analysis according to weeks offered trends where progress of COVID-19 incidence and mortality rate was visible. Pearson coefficient (0.69) suggested moderately strong connection between mortality rate and age, Mann-Whitney (Wilcoxon) test proved statistically significant differences between countries experiencing higher mortality rate and age vs. countries having both indicators lower (p < 0.001). Log-log linear regression analysis defined every increase in life expectancy at birth in total by 1% meant growth in mortality rate by 22% (p < 0.001). CONCLUSION: Spain, Belgium and Ireland, closely followed by Sweden and Great Britain were identified as the worst countries in terms of incidence and mortality rate in the monitored period. Luxembourg, Belarus and Moldova accompanied the group of the worst countries in terms of incidence rate and Italy, France and the Netherland in terms of mortality rate. Correlation analysis and the Mann-Whitney (Wilcoxon) test proved statistically significant positive relationship between mortality rate and age. Log-log linear regression analysis proved that higher age accelerated the growth of mortality rate.