Background: Vascular tumors and malformations, comprising a broad category of lesions often referred to as vascular anomalies. Hemangioma, represents a variety of vascular lesions (both malformations and tumor), while lobular capillary hemangioma is a common vascular lesion of the skin and mucous membranes that occurs mainly in children and young adults. Lymphangiomas are malformations of the lymphatic system. At the level of light microscopy the small lymphatics vessels may be similar to capillaries and sometimes are only tentatively identified by the nature of their contents or by immunohistochemical staining procedure. This study aimed to assess the vascular and lymphatic vessels density in benign vascular lesions using CD34 and D2-40 im

Female infection with HPV (human papilla virus) has been established as an essential cause of CIN (cervical intraepithelial neoplasia). The danger of transformation from CIN to frank malignancy should be considered. Objective: The goal of this study is to evaluate the effectiveness of CO2 laser vaporization of ectocervical lesion high grade squamous intraepithelial lesion (HGSIL). Patients and Methods: Four Female out of 150 affected with HGSIL lesions were submitted to CO2 laser vaporization and followed up in 4 months later, and 10 women with HGSIL lesion submitted to electrocautery diathermy for the comparison. Results: Among women treated by CO2 laser vaporization, 3 women had negative results (clear cervix), at 4 months follow up; o

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod

Background: Epithelial salivary gland tumours are relatively uncommon and constitute a wide spectrum of variable morphologic and biologic entities. The cell proliferation / death balance is most important in the development of salivary gland tumours. The aim of this study was to examine the expression of PCNA protein immunohistochemically and Bax mRNA gene using in situ hybridization techniques and to correlate between the clinicopathological features of salivary gland tumours with the expressions of PCNA protein and Bax mRNA. Materials and Methods: Forty nine formalin fixed paraffin embedded tissue blocks of epithelial salivary gland tumours were used in this study. Haematoxylin and Eosin stain was used for reassessment of the histopath

The research sheds light on an important religious sect that played a fundamental role in the structure of the Iraqi society in general and the Baghdadi society in particular, in which the Jews in general and the Jewish women in particular played an active role in the culture and heritage of Baghdadi society. The educational, cultural and artistic activity of Jewish literacy is a model for study.

This study has dealt with, the issue of classification of rural road network , in addition to prepare a suggested for the classification for this network in Iraq , this classification account , the specifications and characteristics of rural roads, population, and the range taking of settlements , then this classification was applied on the rural road network in the Najaf province there are four categories of classification ,the first is major arterial rural roads divided into two major arterial and minor arterial roads , while the second category collected roads which was divided into minor arterial roads and main collected roads. The third category was represented by Local Roads , it has been divided into paved roads and unpaved, the f

Diabetic retinopathy is an eye disease in diabetic patients due to damage to the small blood vessels in the retina due to high and low blood sugar levels. Accurate detection and classification of Diabetic Retinopathy is an important task in computer-aided diagnosis, especially when planning for diabetic retinopathy surgery. Therefore, this study aims to design an automated model based on deep learning, which helps ophthalmologists detect and classify diabetic retinopathy severity through fundus images. In this work, a deep convolutional neural network (CNN) with transfer learning and fine tunes has been proposed by using pre-trained networks known as Residual Network-50 (ResNet-50). The overall framework of the proposed