Background: Although ultrasonography (US) continues to be the primary imaging modality used to identify and characterize adnexal masses, but certain conditions that hinder accurate ultrasound examination, such as obesity, may be indications for magnetic resonance (MR) imaging, for the assessment of complex and indeterminate ovarian masses.
Objective: to assess the ability of MRI to characterize sonographically indeterminate adnexal masses.
Patients and methods: A prospective study of 89 cases with sonographically indeterminate adnexal mass underwent pelvic MRI conducted in X-ray institute in medical city in Baghdad during period from October 2011 to January 2013 & the results compared to the final diagn

Background: Strain imaging assessing regional myocardial deformation and can be used to quantify regional myocardial function and differentiate between ischemic and non ischemic myocardium.

Objectives: to assess sensitivity and specificity of strain imaging in detection of coronary artery disease in comparison with coronary angiography.
Patients and Methods: ninety six patients were referred to Ibn albitar center for cardiac surgery, Baghdad, Iraq with symptoms of coronary artery disease for a period between June 2014 and April 2015, all of whom were evaluated by two dimensional echocardiography and all were found to have good left ventricular systolic function with no regiona

Background: Conventional  MR imaging is essential for diagnosis and evaluation of the posterior fossa tumors  Objectives: To assess the value of diffusion weighted imaging and apparent diffusion coefficient in making distinction between different histological types of posterior fossa tumors.

Type of the study: Cross-sectional study.

Methods: Brain MRI and DWI assessed 19 patients (12 female and 7 male) with MRI diagnosis of posterior fossa tumors. absolute ADC values of contrast -enhancing solid tumor region and ADC ratio of solid tumor to ADC of normal -appearing deep White matter were compared with histological diagnosis postoperatively .The m

   This research  studyies  wear  rate  of  composite  materials  by using Epoxy Resin and Polyurethane Rubber as a matrix  of weigt percentage (90:10) (Ep/Pu) and reinforced by PVC fibers and Aluminum fibers two dimension knitted mat with fractional volume(15 %), in different conditions like: lab conditions and after submerge the samples in water for different periods of time. . four kinds of materials were prepared: (Ep+pu), (Ep+Pu+PVC), (Ep+Pu+Al.F), (Ep+Pu+PVC+Al. F) .And the results have shown that the best wear resistance are for the hybrid composite material    (Ep + Pu+ PVC + Al. F) and wear rate of  all samples increased when it was submerged in water

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

<span lang="EN-GB">Transmitting the highest capacity throughput over the longest possible distance without any regeneration stage is an important goal of any long-haul optical network system. Accordingly, Polarization-Multiplexed Quadrature Phase-Shift-Keying (PM-QPSK) was introduced lately to achieve high bit-rate with relatively high spectral efficiency. Unfortunately, the required broad bandwidth of PM-QPSK increases the linear and nonlinear impairments in the physical layer of the optical fiber network. Increased attention has been spent to compensate for these impairments in the last years. In this paper, Single Mode Fiber (SMF), single channel, PM-QPSK transceiver was simulated, with a mix of optical and electrical (Digi

This paper proposes improving the structure of the neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Two learning algorithms are used to adjust the parameters weight of the hybrid neural structure with its serial-parallel configuration; the first one is supervised learning algorithm based Back Propagation Algorithm (BPA) and the second one is an intelligent algorithm n

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class