Texture is an important characteristic for the analysis of many types of images because it provides a rich source of information about the image. Also it provides a key to understand basic mechanisms that underlie human visual perception. In this paper four statistical feature of texture (Contrast, Correlation, Homogeneity and Energy) was calculated from gray level Co-occurrence matrix (GLCM) of equal blocks (30×30) from both tumor tissue and normal tissue of three samples of CT-scan image of patients with lung cancer. It was found that the contrast feature is the best to differentiate between textures, while the correlation is not suitable for comparison, the energy and homogeneity features for tumor tissue always greater than its valu

Mammography is at present one of the available method for early detection of masses or abnormalities which is related to breast cancer. The most common abnormalities that may indicate breast cancer are masses and calcifications. The challenge lies in early and accurate detection to overcome the development of breast cancer that affects more and more women throughout the world. Breast cancer is diagnosed at advanced stages with the help of the digital mammogram images. Masses appear in a mammogram as fine, granular clusters, which are often difficult to identify in a raw mammogram. The incidence of breast cancer in women has increased significantly in recent years.
This paper proposes a computer aided diagnostic system for the extracti

Background: Breast cancer account for 29% of all newly diagnosed cancer in female and is responsible for 14% of cancer related deaths in women. Breast cancer is basically detected either during a screening tests, before symptoms have appeared, or after a woman notices a mass. Overall risk doubles each decade until the menopause, when the increase slows down or remains stable.
Objective: to find the correlation between the tumor size and grade and involvement of axillary lymph node.
Patients and methods: a continuous prospective study of 50 patients from 1st January 2016 to 1st January 2017 in Baghdad teaching hospital at 1st surgical floor, where almost all patients with breast cancer operated on by modified radical mastectomy and

      Data mining has the most important role in healthcare for discovering hidden relationships in big datasets, especially in breast cancer diagnostics, which is the most popular cause of death in the world. In this paper two algorithms are applied that are decision tree and K-Nearest Neighbour for diagnosing Breast Cancer Grad in order to reduce its risk on patients. In decision tree with feature selection, the Gini index gives an accuracy of %87.83, while with entropy, the feature selection gives an accuracy of %86.77. In both cases, Age appeared as the  most effective parameter, particularly when Age<49.5. Whereas  Ki67  appeared as a second effective parameter. Furthermore, K- Nearest Neighbor is based on the minimum err

Breast cancer is one of the most common cancers in females. In Iraq there are noticeable elevation in incidence rates and prevalence of advanced stages of breast cancer. Ferritin is intracellular iron storage protein abundant in circulation and its main application in differential diagnosis of anemia.

The level of serum ferritin was found raised in various cancers including breast cancer. The aim of this study was to assess whether the serum ferritin concentration would be altered in Iraqi women with breast cancer and it could be related to progression of disease.

Sixty eight females participated in this study. The mean age of these females was 53.25± 9.52 .The level of serum ferritin was measured in 24

     Breast cancer (BC) is the most common malignancy in women worldwide and a major cause of cancer-related deaths for women in Iraq. This assignment was created to investigate the characteristics of BC diagnosed in Baghdad from 2018 to 2021. A total of eighty-nine of paraffin embedded tissue blocks of different breast tissue tumors (71 females and 18 males) with their data, were collected from archive of Histopathology Department, Teaching Laboratories of Medical City, Al-Yarmouk Teaching Hospital, and a private laboratory in Baghdad-Iraq. The clinical information regarding age, gender, tumor size, tumor stage and grade, lymph nodes metastasis, in addition to the findings of estrogen receptor (ER), progesterone receptor (PR), human

Breast cancer is the commonest cancer affecting women worldwide. Different studies have dealt with the etiological factors of that cancer aiming to find a way for early diagnosis and satisfactory therapy. The present study clarified the relationship between genetic polymorphisms of BRCA1 & BRCA2 genes and some etiological risk factors among breast cancer patients in Iraq. This investigation was carried out on 25 patients (all were females) who were diagnosed as breast cancer patients attended AL-Kadhemya Teaching Hospital in Baghdad and 10 apparently healthy women were used as a control, all women (patients and control) aged above 40 years. The Wizard Promega kit was used for DNA isolation from breast patients and normal individuals. B

Background: Epstein Barr Virus (EBV) infection has been implicated in pathogenesis of several types of carcinomas such as nasopharyngeal carcinoma, gastric cancer and bladder cancer and has recently been associated with breast cancer.
Objective: To evaluate the relations between Epstein Barr virus-encoded small RNA (EBER) and breast cancer.
Methods: Twenty two cases of breast cancer were retrieved from the Al-Kadhimiya Teaching Hospital in Baghdad. Clinical data were analyzed from the medical records and formalin fixed, paraffin embedded tumor tissue were examined by Chromogeneic in situ hybridization (ISH) technique for the detection of EBER.
Results: The expression of EBER in tissues patients with breast cancer in the present

In this research, a group of gray texture images of the Brodatz database was studied by building the features database of the images using the gray level co-occurrence matrix (GLCM), where the distance between the pixels was one unit and for four angles (0, 45, 90, 135). The k-means classifier was used to classify the images into a group of classes, starting from two to eight classes, and for all angles used in the co-occurrence matrix. The distribution of the images on the classes was compared by comparing every two methods (projection of one class onto another where the distribution of images was uneven, with one category being the dominant one. The classification results were studied for all cases using the confusion matrix between every