High spatial resolution (HSR) image segmentation is considered to be a major challenge for object-oriented remote sensing applications that have been extensively studied in the past. In this paper, we propose a fast and efficient framework for multiscale and multifeatured hierarchical image segmentation (MMHS). First, the HSR image pixels were clustered into a small number of superpixels using a simple linear iterative clustering algorithm (SLIC) on modern graphic processing units (GPUs), and then a region adjacency graph (RAG) and nearest neighbors graph (NNG) were constructed based on adjacent superpixels. At the same time, the RAG and NNG successfully integrated spectral information, texture information, and structural information from a small number of superpixels to enhance its expressiveness. Finally, a multiscale hierarchical grouping algorithm was implemented to merge these superpixels using local-mutual best region merging (LMM). We compared the experiments with three state-of-the-art segmentation algorithms, i.e., the watershed transform segmentation (WTS) method, the mean shift (MS) method, the multiresolution segmentation (MRS) method integrated in commercial software, eCognition9, on New York HSR image datasets, and the ISPRS Potsdam dataset. Computationally, our algorithm was dozens of times faster than the others, and it also had the best segmentation effect through visual assessment. The supervised and unsupervised evaluation results further proved the superiority of the MMHS algorithm.
This study is devoted to proposing a useful intelligent prediction model to distinguish the severity of COVID-19, to provide a more fair and reasonable reference for assisting clinical diagnostic decision-making. Based on patients' necessary information, pre-existing diseases, symptoms, immune indexes, and complications, this article proposes a prediction model using the Harris hawks optimization (HHO) to optimize the Fuzzy K-nearest neighbor (FKNN), which is called HHO-FKNN. This model is utilized to distinguish the severity of COVID-19. In HHO-FKNN, the purpose of introducing HHO is to optimize the FKNN's optimal parameters and feature subsets simultaneously. Also, based on actual COVID-19 data, we conducted a comparative experiment between HHO-FKNN and several well-known machine learning algorithms, which result shows that not only the proposed HHO-FKNN can obtain better classification performance and higher stability on the four indexes but also screen out the key features that distinguish severe COVID-19 from mild COVID-19. Therefore, we can conclude that the proposed HHO-FKNN model is expected to become a useful tool for COVID-19 prediction. INDEX TERMSCOVID-19, coronavirus, fuzzy K-nearest neighbor, Harris hawk optimization, disease diagnosis, feature selection. I. INTRODUCTION Coronavirus disease 2019 (COVID-19) is a highly contagious viral disease, and the World Health Organization (WHO) declared that the COVID-19 was an international public health emergency [1], [2]. First described COVID-19 in December 2019 in Wuhan, Hubei Province, China. The ongoing outbreak of COVID-19 is affecting multiple countries in the world [1]. Until Mar 11th, 2020, The associate editor coordinating the review of this manuscript and approving it for publication was Juan Wang . 118,326 cases of COVID-19 were diagnosed worldwide, including 80,955 cases in China and 37,371 cases outside China. Additionally, 4,292 deaths have been triggered by COVID-19 [3]. Many countries are facing increased pressures on health care resources. Up to now, a great deal of studies is focused on using traditional statistical methods to identify risk factors of COVID-19 patients. As an example, older age, pre-existing diseases, abnormal liver function, and T-lymphocyte count were correlated closely with COVID-19 progression and prognosis [4]-[6]. However, traditional statistical methods could not rapidly identify changes
Background It has been reported that a fraction of recovered coronavirus disease 2019(COVID-19) patients have retested positive for SARS-CoV-2. Clinical characteristics and risk factors for retesting positive have not been studied extensively. Methods In this retrospective, single-center cohort study, we included adult patients (≥ 18 years old) diagnosed as COVID-19 in Affiliated Yueqing Hospital, Wenzhou Medical University, Zhejiang, China. All the patients were discharged before March 31, 2020, and were re-tested for SARS-CoV-2 RNA by real-time reverse-transcriptase polymerase-chain-reaction (RT-PCR) after meeting the discharge criteria. We retrospectively analyzed this cohort of 117 discharged patients and analyzed the differences between retest positive and negative patients in terms of demographics, clinical characteristics, laboratory findings, chest computed tomography (CT) features and treatment procedures. Findings Compared with the negative group, the positive group had a higher proportion of patients with comorbidities (Odds Ratio(OR) =2·12, 95% Confidence Interval(CI) 0·48–9·46; p = 0·029), longer hospital stay (OR=1·21, 95% CI 1·07–1·36; p = 0·008), a higher proportion of patients with lymphocytopenia ( p = 0·036), a higher proportion of antibiotics treatment ( p = 0·008) and glucocorticoids treatment ( p = 0·003). Multivariable regression showed increasing odds of positive SARS-CoV-2 retest after discharge associated with longer hospital stay (OR=1·22, 95% CI 1·08–1·38; p = 0·001), and lymphocytopenia (OR=7·74, 95% CI 1·70–35·21; p = 0·008) on admission. Interpretation Patients with COVID-19 who met discharge criteria could still test positive for SARS-CoV-2 RNA. Longer hospital stay and lymphopenia could be potential risk factors for positive SARS-CoV-2 retest in COVID-19 patients after hospital discharge. Funding Natural Science Foundation of Zhejiang Province, Medical Scientific Research Fund of Zhejiang Province, Wenzhou science and technology project
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