Background:This is a prospective study of three children presented to us in the Orbital clinic in AL ShahidGazi Al Hariri Hospital with painless proptosiswith suspension of Hydatid disease.Objectives: : Orbital hydatid disease is a rare lesion accounting for less than 1% of the total lesions of the body (1, 2). Orbital cysts presented as a primary lesion in our study which is rare to have such lesion without involvement of other organs (3). Humans represent the intermediate host where the commonly affected organ are liver and the lung (10-15%) (4). Methods:This is a prospective study of three Children presented to us in the Orbital clinic in Al Shahid Ghazi Alhariri Hospital with painless proptosis with suspension of Hydatid disease, dep

Objective Thalassemic patients present with multiple immune abnormalities that may predispose them to oral Candida, however this has not been investigated. The aim of this study was to assess oral candidal colonization in a group of patients with β-thalassemia major both qualitatively and quantitatively. Study design The oral mycologic flora of 50 β-thalassemia major patients and 50 age- and sex-matched control subjects was assessed using the concentrated oral rinse technique. Candida species were identified using the germ tube test and the Vitek yeast identification system. Results Oral Candida was isolated from 37 patients (74%) and 28 healthy subjects (56%; P = .04). The mean candidal count was significantly higher in thalassemic patie

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

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