The last few years witnessed great and increasing use in the field of medical image analysis. These tools helped the Radiologists and Doctors to consult while making a particular diagnosis. In this study, we used the relationship between statistical measurements, computer vision, and medical images, along with a logistic regression model to extract breast cancer imaging features. These features were used to tell the difference between the shape of a mass (Fibroid vs. Fatty) by looking at the regions of interest (ROI) of the mass. The final fit of the logistic regression model showed that the most important variables that clearly affect breast cancer shape images are Skewness, Kurtosis, Center of mass, and Angle, with an AUCROC of 88% and an Accuracy of almost 89%. We also came to the conclusion that the Fibroid mass is small and less white than the Fatty mass
The Influence of Some Vitamins and Biochemical Parameters on Iraqi Females’ Patients with Malignant Breast Cancer"
A new synthesis of Schiff (K) 6 and Mannich bases (Q) 7 had formed compound (Q) 7 by reacting compound (K) with N-methylaniline at the presence of formalin 35% to given Mannich base (Q). Additionally, new complexes were formed by reacting Schiff base (K) with metal salts CuCl2·2H2O, PdCl2·2H2O, and PtCl6·6H2O by 2:1 of M:L ratio. New ligands and their complexes were characterized, exanimated, and confirmed through several techniques, including FTIR, UV-visible, 1H-NMR, 13C-NMR spectroscopy, CHN analysis, FAA, TG, molar conductivity, and magnetic susceptibility. These compounds and their complexes were screened against breast cancer cells. It was determined that several of these compounds had a significant anti-breast cancer effec
... Show MoreThis present paper aim at knowing the process of evaluating the training program that could be applied in Maysan Health office for it significance and importance in field of management and vocational staff preparations of high scientific experience in different fields of Health. The society of research includes staffs working in Maysan Health Office , of specialists , dentists, pharmacists, laboratories, nursing and administrators. Their number is 100 employees, the researcher has designed questionnaire by depending on "Kirkpatrick" for assessing the training . The researcher has used thorough survey and has entailed 90 questionnaire,
... Show MoreDiabetic 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
... Show MoreBreast cancer is one of the most important malignant diseases all over the world. The incidence of breast cancer is increasing around the world and it is still the leading cause of cancer mortality An Approximately 1.3 million new cases were diagnosed worldwide last year. With areas rising increasing, risk factors for breast cancer including obesity, early menarche, alcohol and smoking, environmental contamination and reduced or late birth rates become more prevalent. In Iraq, breast cancer ranks first among types of cancers diagnosed in women. This study was conducted on one hundred twenty women with breast cancer that was evaluated and investigated for the possible role of the risk factors on the development of breast cancer in females. T
... Show MoreIn 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
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