With its rapid spread, the coronavirus infection shocked the world and had a huge effect on billions of peoples' lives. The problem is to find a safe method to diagnose the infections with fewer casualties. It has been shown that X-Ray images are an important method for the identification, quantification, and monitoring of diseases. Deep learning algorithms can be utilized to help analyze potentially huge numbers of X-Ray examinations. This research conducted a retrospective multi-test analysis system to detect suspicious COVID-19 performance, and use of chest X-Ray features to assess the progress of the illness in each patient, resulting in a "corona score." where the results were satisfactory compared to the benchmarked techniques.  T

We propose a new method for detecting the abnormality in cerebral tissues present within Magnetic Resonance Images (MRI). Present classifier is comprised of cerebral tissue extraction, image division into angular and distance span vectors, acquirement of four features for each portion and classification to ascertain the abnormality location. The threshold value and region of interest are discerned using operator input and Otsu algorithm. Novel brain slices image division is introduced via angular and distance span vectors of sizes 24˚ with 15 pixels. Rotation invariance of the angular span vector is determined. An automatic image categorization into normal and abnormal brain tissues is performed using Support Vector Machine (SVM). St

Background: EBV infection in tissue micro-environment is challenged by the precisely regulated survivaland apoptosis mechanisms. Abnormal bcl-2 proto-oncogene expression in colonic carcinomas allowsaccumulation and propagation of these genetically altered cells.Objective: To analyze the relevant concordance of BCL-2 gene , EBNA1 s and LMP-1-EBV expression inissues from a group of Iraqi patients with colonic adenocarcinomas.Patients and Methods: One hundred (100) tissue biopsies, belonged to (40) patients with colorectalcancers, (40) patients with benign colon tumors, and (20) apparently normal colorectal control tissues,were enrolled in this study. The detection of EBNA1 s and LMP-1-EBV as well as BCL-2 was done byimmunohistochemist

Detergent is one of the pollutants that poses significant threats to ecological systems. Detergents can also dissolve in wastewater and negatively impact the efficiency of wastewater treatment facilities. They are used for a variety of functions, most notably hygiene, and are an integral aspect of human life. This means that there are a variety of routes by which detergent components can reach the environment. In this Study, twenty-three detergent samples from local markets in Baghdad. The aim of this study is to investigate the concentration of heavy metals Cobalt (Co), Chromium (Cr),Lead (Pb),Zinc (Zn), Iron (Fe) and Cadmium (Cd) in some detergents using Atomic Absorption Spectrophotometer. The results of the concentration of heavy elemen

Background: e cerebellum is divided into two hemispheres and contains a narrow midline zone called thevermis. A set of large folds are conventionally used to divide the overall structure into ten smaller "lobules". evermis receives fibres from the trunk and proximal portions of limbs, But the question is that does the cerebellum have the same measurementvalues in males and females of the same age?Material and method: e present study used 80 sectional brain MRI images (40: males, 40: females); 35-50 years old as indices of size for thevermian structures of the Cerebellum. is middle age group was taken because as known generally it could be neither an age of growth as inthe young nor of atrophy as in old individuals. e aim rega

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