Automated COVID-19 Detection from WBC-DIFF Scattergram Images with Hybrid CNN Model Using Feature Selection

In the medical diagnosis such as WBC (white blood cell), the scattergram images show the relationships between neutrophils, eosinophils, basophils, lymphocytes, and monocytes cells in the blood. For COVID-19 detection, the distributions of these cells differ in healthy and COVID-19 patients. This study proposes a hybrid CNN model for COVID-19 detection using scatter images obtained from WBC sub (differential-DIFF) parameters instead of CT or X-Ray scans. As a data set, the scattergram images of 335 COVID-19 suspects without chronic disease, collected from the biochemistry department of Elazig Fethi Sekin City Hospital, are examined. At first, the data augmentation is performed by applying HSV(Hue, Saturation, Value) and CIE-1931(Commission Internationale de l'éclairage) conversions. Thus, three different image large sets are obtained as a result of raw, CIE-1931, and HSV conversions. Secondly, feature extraction is applied by giving these images as separate inputs to the CNN model. Finally, the ReliefF feature extraction algorithm is applied to determine the most dominant features in feature vectors and to determine the features that maximize classification accuracy. The obtaining feature vector is classified with high-performance SVM in binary classification. The overall accuracy is 95.2%, and the F1-Score is 94.1%. The results show that the method can successfully detect COVID -19 disease using scattergram images and is an alternative to CT and X-Ray scans.

Managing Home Health-Care Services With Dynamic Arrivals During a Public Health Emergency

We consider a public health emergency, during which a high number of patients and their varying health conditions necessitate prioritizing patients receiving home health care. Moreover, the dynamic emergence of patients needing urgent care during the day should be handled by rescheduling these patients. In this article, we present a reoptimization framework for this dynamic problem to periodically determine which patients will be visited in which order on each day to maximize the total priority of visited patients and to minimize the overtime for the health-care provider. This optimization framework also aims to minimize total routing time. A mixed-integer programming (MIP) model is formulated and solved at predetermined reoptimization times, to assure that urgent patients are visited within the current day, while visits of others may be postponed, if overtime is not desired or limited. The effectiveness of a schedule is evaluated with respect to several performance metrics, such as the number of patients whose visits are postponed to the next day, waiting time of urgent patients, and required overtime. The MIP-based approach is compared to two practical heuristics that achieve satisfactory performance under a nervous service system by excelling in different criteria. The MIP-based reoptimization approach is demonstrated for a case during the COVID-19 pandemic. We contribute to the home health-care literature by managing dynamic/urgent patient arrivals under a multiperiod setting with prioritized patients, where we optimize different rescheduling objectives via three alternative reoptimization approaches.

COVID-19 Detection on Chest X-ray Images with the Proposed Model Using Artificial Intelligence and Classifiers

Coronavirus disease-2019 (COVID-19) is a serious infectious disease that is spreading rapidly all over the world. Scientists are looking for alternative diagnostic methods to detect and control the disease early. Artificial intelligence applications are promising in the COVID-19 epidemic. This paper proposes a hybrid approach for diagnosing COVID-19 on chest X-ray images and differentiation from other viral pneumonia. The model we propose consists of three steps. In the first step, classification was made using the MobilenetV2, Efficientnetb0, and Darknet53 deep models. In the second step, the feature maps of the images in the Chest X-ray data set were extracted separately for each architecture using the MobilenetV2, Efficientnetb0, and Darknet53 architectures. NCA method was preferred to reduce the size of these feature maps obtained. The feature maps obtained after dimension reduction were classified in the classic machine learning classifiers. In the third step, the feature maps obtained from each architecture were combined. After dimension reduction was applied to these combined features by applying the NCA method, this feature map is classified in the classifiers. The model we proposed was tested on two different data sets. The accuracy values obtained in these data sets are 99.05 and 97.1%, respectively. The obtained accuracy values show that the model is successful. [ FROM AUTHOR] Copyright of New Generation Computing is the property of Springer Nature 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 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 . (Copyright applies to all s.)

Prognostic nutritional index as indicator of immune nutritional status of patients with COVID-19.

Purpose: This study aimed to investigate the effect of the nutritional status, as assessed by the prognostic nutritional index (PNI) on the disease prognosis of patients with COVID-19. Methods: This retrospective study included 282 patients with COVID-19. The PNI score of all patients, 147 of whom were male, with a mean age of 56.4±15.3 years, was calculated. According to the PNI score, the patients with normal and mild malnutrition constituted group-1 (n=159) and the patients with moderate-to-severe and serious malnutrition constituted group-2 (n=123). Results: The PNI score was correlated with age (r=-0.146, p=0.014); oxygen saturation (r=0.190, p=0.001); heart rate (r=-0.117, p=0.05); hospitalization duration (r=-0.266, p<0.001); white blood cells (r=0.156, p=0.009); hemoglobin (r=0.307, p<0.001); C-reactive protein (CRP) (r=-0.346, p<0.001); creatinine (r=-0.184, p=0.002); D-dimer (r=-0.304, p<0.001); ferritin (r=-0.283, p<0.001); procalcitonin (r=-0.287, p<0.001); the confusion, urea, respiratory rate, blood pressure, and age ≥65 years score (r=-0.217, p<0.001); and the quick sequential organ failure assessment score (r=-0.261, p<0.001) in patients with COVID-19. Mortality was significantly higher in Group 2 (p<0.001). Survival was significantly higher if PNI score was >41.2 (p<0.001, sensitivity: 78.7% and specificity: 84.2%). In multivariate regression analysis, among various other parameters, only PNI score and oxygen saturation had a significant effect on the disease course (p=0.02 and p=0.045, respectively). Conclusion: PNI, calculated from the serum albumin concentration and total lymphocyte count, is a simple and objective indicator that assesses the immune nutritional status of patients with COVID-19. The presence of malnutrition has a high predictive value in predicting the severity of COVID-19. Our data suggest that the PNI might be useful for risk stratification of patients with COVID-19 in clinical practice.

The effect of deep feature concatenation in the classification problem: An approach on COVID‐19 disease detection

In image classification applications, the most important thing is to obtain useful features. Convolutional neural networks automatically learn the extracted features during training. The classification process is carried out with the obtained features. Therefore, obtaining successful features is critical to achieving high classification success. This article focuses on providing effective features to enhance classification performance. For this purpose, the success of the process of concatenating features in classification is taken as basis. At first, the features acquired by feature transfer method are extracted from AlexNet, Xception, NASNETLarge, and EfficientNet‐B0 architectures, which are known to be successful in classification problems. Concatenating the features results in the creation of a new feature set. The method is completed by subjecting the features to various classification algorithms. The proposed pipeline is applied to the three datasets: “COVID‐19 Image Dataset,” “COVID‐19 Pneumonia Normal Chest X‐ray (PA) Dataset,” and “COVID‐19 Radiography Database” for COVID‐19 disease detection. The whole datasets contain three classes (normal, COVID, and pneumonia). The best classification accuracies for the three datasets are 98.8%, 95.9%, and 99.6%, respectively. Performance metrics are given such as: sensitivity, precision, specificity, and F1‐score values, as well. Contribution of paper is as follows: COVID‐19 disease is similar to other lung infections. This situation makes diagnosis difficult. Furthermore, the virus's rapid spread necessitates the need to detect cases as soon as possible. There has been an increased curiosity in computer‐aided deep learning models to provide the requirements. The use of the proposed method will be beneficial as it provides high accuracy.

A Deep Learning Based Hybrid Approach for COVID-19 Disease Detections

COVID-19 appeared in December 19, 2019 in Wuhan, China. This disease has spread to almost all countries in a short time. Countries take a series of stringent measures, including the prohibition of going out to prevent the virus that spreads COVID-19 disease. In this paper, we aimed to diagnose COVID-19 disease from X_RAY images by using deep learning architectures. In addition, 96.30% accuracy rate has been achieved with the hybrid architecture we have improved. While developing the hybrid model, the last 5 layers of Resnet 50 architecture were ejected. 10 layers were added in place of the 5 layers that were removed. The count of layers, which is 177 in the Resnet50 architecture, has been increased to 182 in the hybrid model Thanks to these layer changes made in Resnet50, the accuracy rate has been increased more. Classification was performed with AlexNet, Resnet50, GoogLeNet, VGG16 and developed hybrid architectures using COVID-19 Chest X-Ray dataset and Chest X-Ray images (Pneumonia) datasets. As a result, when other scientific works in the literature are examined, it is finalized that the improved hybrid method offers better results than other deep learning architectures and can be used in computer-aided systems to diagnose COVID-19 disease.