Intracranial hemorrhage (ICH) is a serious medical condition that necessitates a prompt and exhaustive medical diagnosis. This paper presents a multi-label ICH classification issue with six different types of hemorrhages, namely epidural (EPD), intraparenchymal (ITP), intraventricular (ITV), subarachnoid (SBC), subdural (SBD), and Some. A patient may experience numerous hemorrhages at the same time in some situations. A CT scan of a patient’s skull is used to detect and classify the type of ICH hemorrhage(s) present. First, our model determines whether there is a hemorrhage or not; if there is a hemorrhage, the model attempts to identify the type of hemorrhage(s). In this paper, we present a hybrid deep learning approach that combines convolutional neural network (CNN) and Long-Short Term Memory (LSTM) approaches (Conv-LSTM). In addition, to propose viable solutions for the problem, we used a Systematic Windowing technique with a Conv-LSTM. To ensure the efficacy of the proposed model, experiments are conducted on the RSNA dataset. The suggested model provides higher sensitivity (93.87%), specificity (96.45%), precision (95.21%), and accuracy (95.14%). In addition, the obtained F1 score results outperform existing deep neural network-based algorithms.

Introduction Awareness of screening procedures and illness warning signals is critical for expanding and implementing screening programs in society, which would improve the odds of early identification of breast cancer. Objectives This study aimed to evaluate the knowledge, awareness, attitudes, and practices related to breast cancer risk factors, signs, symptoms and methods of screening among female faculty and students at Hail University in the Kingdom of Saudi Arabia. Methods A cross-sectional study was conducted from January 2021 through February 2021 in the Hail region of Saudi Arabia. A closed-ended questionnaire, which consisted of 37 questions, was distributed online (using a Google Forms link) in both English and Arabic languages. Data was collected from 425 female subjects who participated in the study. Results The study showed an overall knowledge level of 46.36% regarding breast cancer. Participants had average knowledge about risk factors, signs, and symptoms, whereas their awareness and practice of breast self-examination and screening methods were weak. Conclusion The current study concluded that public awareness of breast cancer remains relatively low, and Saudi Arabia still needs several public awareness initiatives using mass media, such as television, the Internet, and radio, as well as social media. Special awareness programs should also be held in places where a large number of women can easily be reached, such as colleges, universities, and hospitals.

ObjectivesThe acceptance of the COVID-19 vaccine is essential for protecting the world population and stopping the COVID-19 pandemic. This paper aimed to measure public acceptance of the COVID-19 vaccination and the factors that may play an important role in increasing the acceptance of vaccinations in future pandemics.DesignA cross-sectional, observational study was conducted through a survey designed using the Google Forms platform. In this study, a logistic regression analysis was used to study and detect the variables linked to the acceptance of COVID-19 vaccination. To meet inclusion criteria, participants had to be 18 years or older at the time of collecting the data, reside in Saudi Arabia at the time of the survey, agree to the consent form and be able to complete the survey in Arabic.SettingRandomly selected residents of Saudi Arabia.Number of participants1658.ResultsIn general, the population of Saudi Arabia is supportive of the COVID-19 vaccine (72.0%) and has one of the highest acceptance rates, according to global studies. We found that men (OR 0.73; 95% CI: 0.55 to 0.97) were less likely to hesitate with regard to taking the vaccine, whereas previously infected individuals were more likely to hesitate (OR 1.77; 95% CI: 1.25 to 2.50). Those with a lower monthly income (<3000 Saudi riyal) were more likely to refuse the vaccine (OR 3.54; 95% CI: 1.81 to 6.91), while those living in cities (OR 0.62; 95% CI: 0.39 to 0.99) and the unemployed (OR 0.52; 95% CI: 0.33 to 0.83) were less likely to refuse it. Participants’ history of viral infection and trust in the healthcare system were found to be important factors in the public’s acceptance of the vaccine.ConclusionIn general, acceptance of the COVID-19 vaccination is high in Saudi Arabia. Several factors have shown a method for predicting those who might reject the vaccine or hesitate to take it; thus, the healthcare system should target those residents throughout the campaign. Based on the conclusions of the current research, the acceptance of vaccinations could be increased.

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One of the top causes of mortality in people globally is a brain tumor. Today, biopsy is regarded as the cornerstone of cancer diagnosis. However, it faces difficulties, including low sensitivity, hazards during biopsy treatment, and a protracted waiting period for findings. In this context, developing non-invasive and computational methods for identifying and treating brain cancers is crucial. The classification of tumors obtained from an MRI is crucial for making a variety of medical diagnoses. However, MRI analysis typically requires much time. The primary challenge is that the tissues of the brain are comparable. Numerous scientists have created new techniques for identifying and categorizing cancers. However, due to their limitations, the majority of them eventually fail. In that context, this work presents a novel way of classifying multiple types of brain tumors. This work also introduces a segmentation algorithm known as Canny Mayfly. Enhanced chimpanzee optimization algorithm (EChOA) is used to select the features by minimizing the dimension of the retrieved features. ResNet-152 and the softmax classifier are then used to perform the feature classification process. Python is used to carry out the proposed method on the Figshare dataset. The accuracy, specificity, and sensitivity of the proposed cancer classification system are just a few of the characteristics that are used to evaluate its overall performance. According to the final evaluation results, our proposed strategy outperformed, with an accuracy of 98.85%.