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 extraction of features like mass lesions in mammograms for early detection of breast cancer. The proposed technique is based on a four-step procedure: (a) the preprocessing of the image is done, (b) regions of interest (ROI) specification, (c) supervised segmentation method includes two stages performed using the minimum distance (MD) criterion, and (d) feature extraction based on Gray level Co-occurrence matrices GLCM for the identification of mass lesions. The method suggested for the detection of mass lesions from mammogram image segmentation and analysis was tested over several images taken from Al-Ilwiya Hospital in Baghdad, Iraq. The proposed technique shows better results
In this paper, a new tunable approach for fusion the satellite images that fall in different electromagnetic wave ranges is presented, which gives us the ability to make one of the images features little superior on the other without reducing the general resultant image fusion quality, this approach is based on the principal component analysis (PCA) fusion method. A comparison made is between the results of the proposed approach and two fusion methods (they are: the PCA fusion method and the projection of eigenvectors on the bands fusion method), and the comparison results show the validity of this new method.
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In present work the effort has been put in finding the most suitable color model for the application of information hiding in color images. We test the most commonly used color models; RGB, YIQ, YUV, YCbCr1 and YCbCr2. The same procedures of embedding, detection and evaluation were applied to find which color model is most appropriate for information hiding. The new in this work, we take into consideration the value of errors that generated during transformations among color models. The results show YUV and YIQ color models are the best for information hiding in color images.
Suicidal ideation is one of the most severe mental health issues faced by people all over the world. There are various risk factors involved that can lead to suicide. The most common & critical risk factors among them are depression, anxiety, social isolation and hopelessness. Early detection of these risk factors can help in preventing or reducing the number of suicides. Online social networking platforms like Twitter, Redditt and Facebook are becoming a new way for the people to express themselves freely without worrying about social stigma. This paper presents a methodology and experimentation using social media as a tool to analyse the suicidal ideation in a better way, thus helping in preventing the chances of being the victim o
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In order to scrutinize the impact of the decoration of Sc upon the sensing performance of an XN nanotube (X = Al or Ga, and XNNT) in detecting sarin (SN), the density functionals M06-2X, τ-HCTHhyb, and B3LYP were utilized. The interaction of the pristine XNNT with SN was a physical adsorption with the sensing response (SR) of approximately 5.4. Decoration of the Sc metal into the surface of the AlN and GaN led to an increase in the adsorption energy of SN from −3.4 to −18.9, and −3.8 to −20.1 kcal/mol, respectively. Also, there was a significant increase in the corresponding SR to 38.0 and 100.5, the sensitivity of metal decorated XNNT (metal@XNNT) is increased. So, we found that Sc-decorating more increases the sensitivity of GaNN
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This research describes a new model inspired by Mobilenetv2 that was trained on a very diverse dataset. The goal is to enable fire detection in open areas to replace physical sensor-based fire detectors and reduce false alarms of fires, to achieve the lowest losses in open areas via deep learning. A diverse fire dataset was created that combines images and videos from several sources. In addition, another self-made data set was taken from the farms of the holy shrine of Al-Hussainiya in the city of Karbala. After that, the model was trained with the collected dataset. The test accuracy of the fire dataset that was trained with the new model reached 98.87%.
The biomarker significance of three chemokines (CXCL8, CXCL10 and CXCL16) was evaluated in sera of 45 breast cancer (BC) and 28 benign breast lesion (BBL) patients, as well as 20 control women. Clinical stage and tumor expression of estrogen (ER), progesterone (PgR) and human epidermal growth factor receptor-2 (HER-2) receptors were considered in this evaluation. The results demonstrated that CXCL8, CXCL10 and CXCL16 showed a significant increased median in BC and BBL patients compared to control (CXCL8: 47.3 and 25.7 vs. 15.0; CXCL10: 37.6 and 30.7 vs. 13.1; CXCL16; 27.9 and 25.2 vs. 19.2 pg/ml, respectively). The increased levels of CXCL8 and CXCL16 were more pronounced in triple-negative and HER-2 positive p
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Asthma and obesity are both a major public health problems affecting large numbers of individuals across the globe. Link between obesity and asthma is now considered as a recognized fact, and many epidemiological studies, found that overweight and obese people had a higher chance of developing asthma, with more severe symptoms. Assessment of the relationship between body mass index and asthma control. A cross-sectional study, that included 100 patients diagnosed with asthma, attending the respiratory disease consultatory unit at Baghdad teaching hospital. Body mass index was calculated by (BMI= weight in Kg/Height in m2), and Asthma control was assessed using asthma control test questionnaire forma. Statistical analysis done using, Test of
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Background. Body mass index (BMI) is a person's weight in kilograms (or pounds) divided by the square of height in meters (or feet). Obesity affects a wide spectrum of age groups, from the young to the elderly, and there are several eye diseases related to obesity like diabetic retinopathy, floppy eyelid syndrome, retinal vein occlusion, stroke-related vision loss, age-related macular degeneration, and possibly, refractive errors. Refractive errors (RE) are optical imperfections related to the focusing ability of the eye and are the main cause of visual impairment which may result in missed education and employment opportunities, lower productivity and impaired quality of life. Aim. The study aimed to find an association between bod
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ABSTRACT Background:- White spot lesions are common esthetic problem that compromise the success of orthodontic treatment. This study aimed to assess white spot lesions in patients with fixed orthodontic appliance at different time intervals. Materials & Methods:- Thirty two patients (24 females and 8 males) were included in this study and they underwent clinical examination for white spot lesions using enamel decalcification index at four time intervals: (2-3 weeks after appliance insertion, 2, 4 and 6 months). Results:- The patients were free of white spot lesions at the appliance insertion visit. The mean of white spot lesions was 2.22 which were increased significantly during six months to reach 24.59 at the end of study. There was a si
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