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Since the Pre-Roman era, olive trees have a significant economic and cultural value. In 2019, the Al-Jouf region, in the north of the Kingdom of Saudi Arabia, gained a global presence by entering the Guinness World Records, with the largest number of olive trees in the world. Olive tree detecting and counting from a given satellite image are a significant and difficult computer vision problem. Because olive farms are spread out over a large area, manually counting the trees is impossible. Moreover, accurate automatic detection and counting of olive trees in satellite images have many challenges such as scale variations, weather changes, perspective distortions, and orientation changes. Another problem is the lack of a standard database of olive trees available for deep learning applications. To address these problems, we first build a large-scale olive dataset dedicated to deep learning research and applications. The dataset consists of 230 RGB images collected over the territory of Al-Jouf, KSA. We then propose an efficient deep learning model (SwinTUnet) for detecting and counting olive trees from satellite imagery. The proposed SwinTUnet is a Unet-like network which consists of an encoder, a decoder, and skip connections. Swin Transformer block is the fundamental unit of SwinTUnet to learn local and global semantic information. The results of an experimental study on the proposed dataset show that the SwinTUnet model outperforms the related studies in terms of overall detection with a 0.94% estimation error. [ABSTRACT FROM AUTHOR]

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Objective: To assess the epidemiological pattern and correlates with the clinical outcome of Cerebral venous thrombosis (CVT) in Abha, Kingdom of Saudi Arabia., Methods: A retrospective record_based cohort design was conducted including all patients admitted with diagnosis of CVT in 2 main tertiary hospitals in Aseer Region between 2015 to the end of 2018. The study hospitals were Aseer Central Hospital and Armed Forces Hospitals Southern Region. The data were collected by structured data sheets, including sociodemographic data. Assessment of known risk factors for CVT, clinical presentation, treatment received, and clinical outcome after treatment were extracted., Results: The study included 119 patients with CVT, whose ages ranged from 15 to 97 years, with a mean age of 35.5-/+14.1 years. Majority of the patients were females (81.5%). Headache was the most presenting (82.4%) symptom, followed by vomiting (30.3%) and a decreased level of consciousness. Thirty_three cases (27.7%) had complications, and recanalization was recorded among 92 cases (94.8%) based on follow up vascular imaging., Conclusion: The study revealed that most of the cases of CVT had favorable clinical outcome and recanalization, especially those who had a shorter duration untildiagnosis. Young females were the most affected group.