In this paper, the discriminant analysis is used to classify the most wide spread heart diseases known as coronary heart diseases into two groups (patient, not patient) based on the changes of discrimination features of ten predictor variables that we believe they cause the disease . A random sample for each group is employed and the stepwise procedures are performed in order to delete those variables that are not important for separating the groups. Tests of significance of discriminant analysis and estimating the misclassification rates are performed

    This study was conducted to investigate the prevalence of anemia among  students (males and females) of the Technical and Medical Institute/ Al-Mansoor, Baghdad. 135 students aged 20 -22 years were subjected. Twenty-one students were found to be anemic. The prevalence of anemia among students was 15.55%, with no significant difference to sex. Then, the anemic students were treated with tablets of ferrous sulfate twice daily for 60 days. The treatment markedly elevated the hemoglobin concentration of both males and females and decreased the prevalence of anemia from 15.55% to 5.92%. The decrease in prevalence of anemia tended to be higher in anemic females as than anemic males (15.51% and 15.58% before treatment an

Objectives: To highlight the knowledge of Al-Kindy medical students regarding varicella infection and to assess their knowledge and attitude toward its prevention. Methodology: This descriptive cross-sectional study enrolled academic year 2020/2021 students at Al-Kindy College of Medicine from first to final year. We used an online questionnaire by Google forms to collect data, which were analyzed with SPSS 26. Results: A total of 208 students participated with 126 in the preclinical and 82 in the clinical grades. Generally, most clinical students were more aware about epidemiology, clinical manifestations, and complications of varicella infection than the preclinical students. Conclusion: Educational stage is a significant variab

In this paper, membrane-based computing image segmentation, both region-based and edge-based, is proposed for medical images that involve two types of neighborhood relations between pixels. These neighborhood relations—namely, 4-adjacency and 8-adjacency of a membrane computing approach—construct a family of tissue-like P systems for segmenting actual 2D medical images in a constant number of steps; the two types of adjacency were compared using different hardware platforms. The process involves the generation of membrane-based segmentation rules for 2D medical images. The rules are written in the P-Lingua format and appended to the input image for visualization. The findings show that the neighborhood relations between pixels o

A   snake   is   an   energy-minimizing   spline   guided   by   external

constraint forces and influenced by image forces that pull it toward features such as lines and edges. Snakes are active contour models: they lock onto nearby edges, localizing them accurately. Snakes provide a unified account of a number of visual problems, including detection of edges, lines, and motion tracking. We have used snakes successfully for segmentation, in  which  user-imposed  constraint forces guide the snake near features of interest (anatomical structures). Magnetic Resonance Image (MRI) data set and Ultrasound images are used for our experiments.

BACKGROUND: Mental health problems are reflected and linked to human behavior in many aspects. Medical students are susceptible to a wide variety of events that compromise their mental well-being, social life as well as their academic achievements. AIM: This study aimed to find the impact of social support on medical students’ behavior in Iraq via assessing their depression, anxiety, and stress status. METHODS: A cross-sectional online survey-based study targeted all medical students in Iraq. The employed questionnaires covered mental health status of participants by evaluating their perceptions of depression, anxiety, and stress using. Data were analyzed using the Statistical Analysis System. RESULTS: The study revealed a signifi

This paper presents a method to classify colored textural images of skin tissues. Since medical images havehighly heterogeneity, the development of reliable skin-cancer detection process is difficult, and a mono fractaldimension is not sufficient to classify images of this nature. A multifractal-based feature vectors are suggested hereas an alternative and more effective tool. At the same time multiple color channels are used to get more descriptivefeatures.Two multifractal based set of features are suggested here. The first set measures the local roughness property, whilethe second set measure the local contrast property.A combination of all the extracted features from the three colormodels gives a highest classification accuracy with 99.4

 

    The combination of wavelet theory and neural networks has lead to the development of wavelet networks. Wavelet networks are feed-forward neural networks using wavelets as activation function. Wavelets networks have been used in classification and identification problems with some success.

  In this work we proposed a fuzzy wavenet network (FWN), which learns by common back-propagation algorithm to classify medical images. The library of medical image has been analyzed, first. Second, Two experimental tables’ rules provide an excellent opportunity to test the ability of fuzzy wavenet network due to the high level of information variability often experienced with this type of images.

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