Since the increasing risk of COVID-19, a set of actions have been achieved to develop tools to handle the spreading of the COVID-19 disease. Though testing kits were being used to diagnose the COVID19 infection, the process requires time and the test kits suffer from being lack. In COVID-19 management, the computed tomography (CT) is considered an important diagnostic method. Taking into account large number of exams performed in high case-load situations, an automated method may help to encourage and save time for diagnosing and identifying the disease. Several deep learning tools have recently been developed for COVID-19 scanning in CT scans as a technique for COVID-19 automation and diagnostic assistance. This article aims to explore the rapid recognition of COVID-19 and proposes an advanced deep learning technique, derived from improving the ResNet architecture as a transfer learning model. The architecture design of the proposed model is based on alleviating the connections between the blocks of the ResNet-50 model. This reduces the training time for scale-ability and handles the problem of vanishing gradient with relevant features for recognizing COVID-19 from CT images. The proposed model is evaluated using two well-known datasets of COVID-19 CT examined with a patient-based split. The proposed model attains a total back- bone accuracy of 98.1% with 97%, and 98.6% specificity and sensitivity, respectively.

Recently, the whole world was hit by COVID-19 pandemic that led to health emergency everywhere. During the peak of the early waves of the pandemic, medical and healthcare departments were overwhelmed by the number of COVID-19 cases that exceeds their capacity. Therefore, new rules and techniques are urgently required to help in receiving, filtering and diagnosing patients. One of the decisive steps in the fight against COVID-19 is the ability to detect patients early enough and selectively put them under special care. Symptoms of this disease can be observed in chest X-rays. However, it is sometimes difficult and tricky to differentiate “only” pneumonia patients from COVID-19 patients. Machine-learning can be very helpful in carrying out this task. In this paper, we tackle the problem of COVID-19 diagnostics following a data-centric approach. For this purpose, we construct a diversified dataset of chest X-ray images from publicly available datasets and by applying data augmentation techniques. Then, we employ a transfer learning approach based on a pre-trained convolutional neural network (DenseNet-169) to detect COVID-19 in chest X-ray images. In addition to that, we employ Gradient-weighted Class Activation Mapping (GradCAM) to provide visual inspection and explanation of the predictions made by our deep learning model. The results were evaluated against various metrics such as sensitivity, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV) and the confusion matrix. The resulting models has achieved an average detection accuracy close to 98.82%. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

In response to the growing threat posed by COVID-19, several initiatives have been launched to develop methods of halting the progression of the disease. In order to diagnose the COVID-19 infection, testing kits were utilized; however, the use of these kits is time-consuming and suffers from a lack of quality control measures. Computed tomography is an essential part of the diagnostic process in the treatment of COVID-19 (CT). The process of disease detection and diagnosis could be sped up with the help of automation, which would cut down on the number of exams that need to be carried out. A number of recently developed deep learning tools make it possible to automate the Covid-19 scanning process in CT scans and provide additional assistance. This paper investigates how to quickly identify COVID-19 using computational tomography (CT) scans, and it does so by using a deep learning technique that is derived from improving ResNet architecture. In order to test the proposed model, COVID-19 CT scans that include a patient-based split are utilized. The accuracy of the model’s core components is 98.1%, with specificity at 97% and sensitivity at 98.6%.

Early diagnosis of transplanted kidney function requires precise Kidney segmentation from Dynamic Contrast-Enhanced Magnetic Resonance Imaging images as a preliminary step. In this regard, this paper aims to propose an automated and accurate DCE-MRI kidney segmentation method integrating fuzzy c-means (FCM) clustering and Markov random field modeling into a level set formulation. The fuzzy memberships, kidney’s shape prior model, and spatial interactions modeled using a second-order MRF guide the LS contour evolution towards the target kidney. Several experiments on real medical data of 45 subjects have shown that the proposed method can achieve high and consistent segmentation accuracy regardless of where the LS contour was initialized. It achieves an accuracy of 0.956 ± 0.019 in Dice similarity coefficient (DSC) and 1.15 ± 1.46 in 95% percentile of Hausdorff distance (HD95). Our …

Linear regression classification (LRC) has proven to be a successful recognition tool in recent years. LRC depends on using the least square algorithm to get the solution of the linear regression equation. To improve the performance of the LRC algorithm, in this paper, we extend the LRC strategy to both quaternion and reduced biquaternion domains to consider image color information. We derive closed-form solutions from the properties of both domains . We also improve on the accuracy of the closed-form solutions using nonlinear optimization. Our experiments on three benchmark color face recognition databases demonstrate the effectiveness of the proposed methods for recognizing color faces.