Introduction: Computer vision models have been used to diagnose some disorders using computer tomography (CT) and magnetic resonance (MR) images. In this work, our objective is to detect large and small brain vessel occlusion using a deep feature engineering model in acute of ischemic stroke. Methods: We use our dataset. which contains 324 patient’s CT images with two classes; these classes are large and small brain vessel occlusion. We divided the collected image into horizontal and vertical patches. Then, pretrained AlexNet was utilized to extract deep features. Here, fc6 and fc7 (sixth and seventh fully connected layers) layers have been used to extract deep features from the created patches. The generated features from patches have been concatenated/merged to generate the final feature vector. In order to select the best combination from the generated final feature vector, an iterative selector (iterative neighborhood component analysis—INCA) has been used, and this selector has chosen 43 features. These 43 features have been used for classification. In the last phase, we used a kNN classifier with tenfold cross-validation. Results: By using 43 features and a kNN classifier, our AlexNet-based deep feature engineering model surprisingly attained 100% classification accuracy. Conclusion: The obtained perfect classification performance clearly demonstrated that our proposal could separate large and small brain vessel occlusion detection in non-contrast CT images. In this aspect, this model can assist neurology experts with the early recanalization chance.

BACKGROUND Restless legs syndrome (RLS) is a chronic condition that affects movement and sensation, making it one of the most common nerve-related disorders. Single-gene polymorphisms (SNP) that alter the expression of the dopamine beta-hydroxylase (DBH) enzyme gene, which converts dopamine to noradrenaline, also appear to affect the disease. In this study we investigated the relationship between polymorphisms in DBH gene SNPs (rs129882, rs161115, and rs732833) and the susceptibility to RLS among the Turkish population. MATERIAL AND METHODS This prospective, case-control study included 103 Turkish patients with RLS and 100 healthy individuals of similar age. The study included RLS patients who met the criteria set by the International RLS Study Group (IRLSSG). DNA was extracted from blood samples taken from the participants, and all cases and controls were genotyped. RESULTS We found the rs732833 DBH CT and homozygous TT genotype frequency was significantly different between the patient and control groups (P<0.05). Patients with mild disease severity showed a statistically significant variation between C and T alleles of rs1611115 DBH (P=0.008). The C allele is more associated with mild severity compared to the T allele. CONCLUSIONS We showed that the rs732833 DBH polymorphism predisposes individuals to RLS and mild disease severity is more commonly associated with the C allele of rs1611115 DBH than with the T allele. To the best of our knowledge, this is the first study investigating the relationship between RLS and DBH gene polymorphisms.