The COVID-19 diffused quickly throughout the world and converted as a pandemic. It has caused a destructive effect on both regular lives, common health and global business. It is crucial to identify positive patients as shortly as desirable to limit this epidemic’s further diffusion and to manage immediately affected cases. The demand for quick assistant distinguishing devices has developed. Recent findings achieved utilizing radiology imaging systems propose that such images include salient data about the COVID-19. The utilization of progressive artificial intelligence (AI) methods linked by radiological imaging can help the reliable diagnosis of COVID-19. As radiography images can recognize pneumonia infections, this research brings an accurate and automatic technique based on a deep residual network to analyze chest X-ray images to monitor COVID-19 and diagnose verified patients. The physician states that it is significantly challenging to separate COVID-19 from common viral and bacterial pneumonia, while COVID-19 is additionally a variety of viruses. The proposed network is expanded to perform detailed diagnostics for two multi-class classification (COVID-19, Normal, Viral Pneumonia) and (COVID-19, Normal, Viral Pneumonia, Bacterial Pneumonia) and binary classification. By comparing the proposed network with the popular methods on public databases, the results show that the proposed algorithm can provide an accuracy of 92.1% in classifying multi-classes of COVID-19, normal, viral pneumonia, and bacterial pneumonia cases. It can be applied to support radiologists in verifying their first viewpoint.

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Hematoma in different parts of the brain is one of the most important complications of head injury and is associated with high mortality and morbidity rate. The aim of this study was evaluation of the relationship between Computed Tomography (CT) and intraoperative findings with clinical symptoms in head trauma patients. In this study 95 patients with cerebral hemorrhage due to head trauma, referred to Taleghani Hospital in Kermanshah were studied. After an initial clinical examination, the level of consciousness determined according to the Glasgow Coma Scale (GCS) was recorded. All patients underwent brain CT scan and findings were recorded, including size and location of the hematoma. Patients in all treatment such as surgical procedures under the supervision, and the information on their status was recorded until discharge or death. It was found that most patients (38%) were between 40-20 years: 73% of patients were male, while 27% were female. The outcome of 35 patients (35.4%) were normal, 12 patients (12.3%) had moderate disability, 9 patients (9.2%) had severe disability, 11 patients (10.8%) vegetative state and 31 patients (32.3%) died. There was a significant association between location of the hematoma and hematoma in CT scan and outcome of patients with cerebral hemorrhage caused by trauma (P<0.05). We also found a significant association between size of the hematoma and midline shift in CT scan with outcome of patients with cerebral hemorrhage caused by trauma (P<0.05). The prognosis of patients with traumatic brain injury depends on location of the hematoma; volume of hematoma, midline shift in CT scan and length of trauma to surgery more than 4 hours.