The last few years witnessed great and increasing use in the field of medical image analysis. These tools helped the Radiologists and Doctors to consult while making a particular diagnosis. In this study, we used the relationship between statistical measurements, computer vision, and medical images, along with a logistic regression model to extract breast cancer imaging features. These features were used to tell the difference between the shape of a mass (Fibroid vs. Fatty) by looking at the regions of interest (ROI) of the mass. The final fit of the logistic regression model showed that the most important variables that clearly affect breast cancer shape images are Skewness, Kurtosis, Center of mass, and Angle, with an AUCROC of

Background: Stroke is an acute neurologic injury and represents the 2^nd leading cause of mortality worldwide, and also the most leading cause of acquired disability and morbidity in adults.

Objective: Effect and association between stroke and risk factors.

Type of the study: A retrospective study.

Methods: The study conducted on 312 patients in 2016, all data were collected from patients’ files from the emergency unit, which included basic demographic and disease characteristic, co morbid diseases, risk factors, final diagnosis.

Results: both previous stroke, ischemic heart disease was strong predictor of new

Abstract Introduction: Aphthous stomatitis is a recurring oral ulceration condition that affects a significant proportion of the population. While several factors have been proposed to contribute to its occurrence, the link between caffeine consumption, anxiety levels, and the development of aphthous stomatitis remains unknown. This survey-based study aimed to understand the association between coffee, anxiety, and the development of aphthous stomatitis in this specific demographic, which can provide useful insights for medical management as well as preventive strategies. Material and methods: A structured online questionnaire was distributed via various social media platforms targeting a sample of the Armenian population. The quest

<p>Currently, breast cancer is one of the most common cancers and a main reason of women death worldwide particularly in<strong> </strong>developing countries such as Iraq. our work aims to predict the type of tumor whether benign or malignant through models that were built using logistic regression and neural networks and we hope it will help doctors in detecting the type of breast tumor. Four models were set using binary logistic regression and two different types of artificial neural networks namely multilayer perceptron MLP and radial basis function RBF. Evaluation of validated and trained models was done using several performance metrics like accuracy, sensitivity, specificity, and AUC (area under receiver ope

  Interleukin -33 is a new member of the IL-1 superfamily of cytokines that is expressed mainly by stromal cells.Its expression is upregulated following pro-inflammatory stimulation. Aim of the present study was to assess the serum IL-33 level and its relationship with inflammatory biomarker CRP  in Iraqi females patients with celiac disease.  Thirty five patients with celiac disease (CD)  and thirty healthy individuals as control group  were enrolled in this study,their age ranged (20-35) year.Anti-Gliadin IgA ,IgG and Anti-Tissue IgA ,IgG were estimated in all subjects as  diagnostic parameters .ESR and CRP were assayed as inflammatory biomarkers. IL-33 was determined in patients and control groups.