Background The novel coronavirus disease 2019 (COVID-19) constitutes a public health emergency globally. The number of infected people and deaths are proliferating every day, which is putting tremendous pressure on our social and healthcare system. Rapid detection of COVID-19 cases is a significant step to fight against this virus as well as release pressure off the healthcare system. Objective One of the critical factors behind the rapid spread of COVID-19 pandemic is a lengthy clinical testing time. The imaging tool, such as Chest X-ray (CXR), can speed up the identification process. Therefore, our objective is to develop an automated CAD system for the detection of COVID-19 samples from healthy and pneumonia cases using CXR images. Methods Due to the scarcity of the COVID-19 benchmark dataset, we have employed deep transfer learning techniques, where we examined 15 different pre-trained CNN models to find the most suitable one for this task. Results A total of 860 images (260 COVID-19 cases, 300 healthy and 300 pneumonia cases) have been employed to investigate the performance of the proposed algorithm, where 70% images of each class are accepted for training, 15% is used for validation, and rest is for testing. It is observed that the VGG19 obtains the highest classification accuracy of 89.3% with an average precision, recall, and F1 score of 0.90, 0.89, 0.90, respectively. Conclusion This study demonstrates the effectiveness of deep transfer learning techniques for the identification of COVID-19 cases using CXR images.

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Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches.

Hybrid fuzzy-disturbance observer for estimating disturbance in styrene polymerization process

This article proposes an automatic method based on a combination of active shape model (ASM) and nonlinear support vector machine (SVM) accompanied with Bayesian information criterion which is utilized in order to measure radiomorphometric indices for digital X‐ray panoramic system. After omitting Poisson noise of input images, the image is divided. It attempts to choose suitable region of interest (ROI) in order to extract indices. This region becomes small and smaller during some steps until the final ROI is found and the mandible's cortex and ramus are detected. Finally, because of measuring the required parameters, ASM and SVM are used in order to evaluate exact setting of the detected region. Experimental results show no significant differences between right and left sides in terms of mental index (MI), panoramic mandibular index (PMI), gonial angle (GA), gonial index (GI), antegonial index (AGI), ramus height (RH), and ramus width (RW); however, in terms of GI and AGI for both sexes. There are significant differences between men and women for all indices in the mandible (P‐value <.05). There was a mild negative significant correlation among GA and MI, GI, AGI and a moderate negative significant correlation among GA and PMI, RH, RW for both genders (P‐value <.05). Besides, it has been shown measuring the panoramic indices provide useful information about trauma and prediction of asymptomatic subjects with osteoporosis at an earlier step.

Measuring panoramic radiomorphometric indices for mandible bone using active shape model and Bayesian information criterion‐support vector machine

A novel method based on Wiener filter for denoising Poisson noise from medical X-Ray images

Performance evaluation of fuzzy genetic, fuzzy particle swarm and similar insects’ optimization algorithms on denoising problem based on novel combined filter for digital X-ray and CT images in Pelvic Region

Fuzzy genetic-based noise removal filter for digital panoramic X-ray images

Background: When an epidemic occurs, resources, including hospital beds, are severely limited and not all patients can be treated; so, hospital bed rationing is inevitable. Therefore, the aim of this study is to provide a method for evaluating and prioritizing patients with COVID-19 for admission to the intensive care unit. 
Methods: This was a descriptive-survey study in terms of data collection method conducted in the summer of 2021. Following the formation of an expert panel with 4 specialists, a three-stage approach to evaluating and prioritizing COVID-19 patients was adopted by combining multi-criteria decision analysis methods. In the first stage, effective criteria and sub-criteria for patients’ prioritization were identified by a panel of experts and related studies, and the hierarchy of criteria was drawn. In the second stage, the Fuzzy best-worst method was used to determine the weight of criteria and sub-criteria. Finally, in the third stage, a combined compromise solution method was performed to prioritize 10 patients in need of COVID-19 and the most critical patient was selected. 
Results: According to the experts, among the 15 criteria studied, respiratory rate, PaO2/FiO2, impaired consciousness, mean arterial pressure and age were identified as 5 of the most important criteria in prioritizing COVID-19 patients for intensive care unit admission. In addition, 10 COVID-19 patients referred to Shohadaye Pakdasht Hospital were evaluated and prioritized with the proposed approach. 
Conclusion: The proposed method can be used as a useful tool in the evaluation of COVID-19 patients for admission to the intensive care unit and to support the vital decisions of physicians.

Prioritization of COVID-19 Patients for Admission in the Intensive Care Unit in the Context of Hospital Bed Shortages: An Integrated Multi-criteria Decision Making Approach

Mahdi Erfanian Taghvaei

Papers

Fuzzy genetic-based noise removal filter for digital panoramic X-ray images

Prioritization of COVID-19 Patients for Admission in the Intensive Care Unit in the Context of Hospital Bed Shortages: An Integrated Multi-criteria Decision Making Approach