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). Standard Deviation, Mean, Energy and Entropy are extorted using the histogram approach for each merger space. These features are found to be higher in occurrence in the tumor region than the non-tumor one. MRI scans of the five brains with 60 slices from each are utilized for testing the proposed method’s authenticity. These brain images (230 slices as normal and 70 abnormal) are accessed from the Internet Brain Segmentation Repository (IBSR) dataset. 60% images for training and 40% for testing phase are used. Average classification accuracy as much as 98.02% (training) and 98.19% (testing) are achieved.
Non Uniform Illumination biological image often leads to diminish structures and inhomogeneous intensities of the image. Algorithm has been proposed using Morphological Operations different types of structuring elements including (dick, line, square and ball) with the same parameters of (15).To correct the non-uniform illumination and enhancement biological images, the non-uniform background illumination have been removed from image, using (contrast adjustment, histogram equalization and adaptive histogram equalization). The used basic approach to extract the statistical features values from gray level of co-occurrence matrices (GLCM) can show the typical values for features content of biological images that can be in form of shape or sp
... Show MoreCOVID-19 (Coronavirus disease-2019), commonly called Coronavirus or CoV, is a dangerous disease caused by the SARS-CoV-2 virus. It is one of the most widespread zoonotic diseases around the world, which started from one of the wet markets in Wuhan city. Its symptoms are similar to those of the common flu, including cough, fever, muscle pain, shortness of breath, and fatigue. This article suggests implementing machine learning techniques (Random Forest, Logistic Regression, Naïve Bayes, Support Vector Machine) by Python to classify a series of chest X-ray images that include viral pneumonia, COVID-19, and healthy (Not infected) cases in humans. The study includes more than 1400 images that are collected from the Kaggle platform. The expe
... Show MoreInternal Audit is one of the most important backers of corporate governance, the researcher expanded his interest in this subject to examine the efficiency of Internal Auditors at the Arab Bank and its branches in Jordan to achieve Accountability which enhances the Corporate Governance and to identify the effect of the International Internal Audit Standards in strengthening the role of Internal Auditors in Accountability, and the effect of Attribute and Performance Standards in Accountability. The researcher applied descriptive analysis method to define the role of Internal Audit in the Arab Bank in achieving one of the basic principles of Corporate Governance assimilated in Accountability. The researcher’s sources include
... Show MoreAbstract—In this study, we present the experimental results of ultra-wideband (UWB) imaging oriented for detecting small malignant breast tumors at an early stage. The technique is based on radar sensing, whereby tissues are differentiated based on the dielectric contrast between the disease and its surrounding healthy tissues. The image reconstruction algorithm referred to herein as the enhanced version of delay and sum (EDAS) algorithm is used to identify the malignant tissue in a cluttered environment and noisy data. The methods and procedures are tested using MRI-derived breast phantoms, and the results are compared with images obtained from classical DAS variant. Incorporating a new filtering technique and multiplication procedure, t
... Show MoreCyberbullying is one of the major electronic problems, and it is not a new phenomenon. It was present in the traditional form before the emergence of social networks, and cyberbullying has many consequences, including emotional and physiological states such as depression and anxiety. Given the prevalence of this phenomenon and the importance of the topic in society and its negative impact on all age groups, especially adolescents, this work aims to build a model that detects cyberbullying in the comments on social media (Twitter) written in the Arabic language using Extreme Gradient Boosting (XGBoost) and Random Forest methods in building the models. After a series of pre-processing, we found that the accuracy of classification of t
... Show MoreABSTRACT:
Microencapsulation is used to modify and retard drug release as well as to overcome the unpleasant effect
(gastrointestinal disturbances) which are associated with repeated and overdose of ibuprofen per day.
So that, a newly developed method of microencapsulation was utilized (a modified organic method) through a
modification of aqueous colloidal polymer dispersion method using ethylcellulose and sodium alginate coating materials to
prepare a sustained release ibuprofen microcapsules.
The effect of core : wall ratio on the percent yield and encapsulation efficiency of prepared microcapsules was low, whereas
, the release of drug from prepared microcapsules was affected by core: wall ratio ,proportion of coa
The use of remote sensing technologies was gained more attention due to an increasing need to collect data for the environmental changes. Satellite image classification is a relatively recent type of remote sensing uses satellite imagery to indicate many key environment characteristics. This study aims at classifying and extracting vacant lands from high resolution satellite images of Baghdad city by supervised Classification tool in ENVI 5.3 program. The classification accuracy was 15%, which can be regarded as fairly acceptable given the difficulty of differentiating vacant land surfaces from other surfaces such as roof tops of buildings.
The most common artifacts in ultrasound (US) imaging are reverberation and comet-tail. These are multiple reflection echoing the interface that causing them, and result in ghost echoes in the ultrasound image. A method to reduce these unwanted artifacts using a Otsu thresholding to find region of interest (reflection echoes) and output applied to median filter to remove noise. The developed method significantly reduced the magnitude of the reverberation and comet-tail artifacts. Support Vector Machine (SVM) algorithm is most suitable for hyperplane differentiate. For that, we use image enhancement, extraction of feature, region of interest, Otsu thresholding, and finally classification image datasets to normal or abnormal image.
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