Alzheimer's disease (AD) increasingly affects the elderly and is a major killer of those 65 and over. Different deep-learning methods are used for automatic diagnosis, yet they have some limitations. Deep Learning is one of the modern methods that were used to detect and classify a medical image because of the ability of deep Learning to extract the features of images automatically. However, there are still limitations to using deep learning to accurately classify medical images because extracting the fine edges of medical images is sometimes considered difficult, and some distortion in the images. Therefore, this research aims to develop A Computer-Aided Brain Diagnosis (CABD) system that can tell if a brain scan exhibits indications of Alzheimer's disease. The system employs MRI and feature extraction methods to categorize images. This paper adopts the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset includes functional MRI and Positron-Version Tomography scans for Alzheimer's patient identification, which were produced for people with Alzheimer's as well as typical individuals. The proposed technique uses MRI brain scans to discover and categorize traits utilizing the Histogram Features Extraction (HFE) technique to be combined with the Canny edge to representing the input image of the Convolutional Neural Networks (CNN) classification. This strategy keeps track of their instances of gradient orientation in an image. The experimental result provided an accuracy of 97.7% for classifying ADNI images.
Objective: The study aimed to screen the prepubertal children for idiopathic scoliosis at earlier stages, and find
out the relationship between idiopathic scoliosis and demographic data such as age, sex, body mass index,
heavy backpacks, and heart & lung diseases.
Methodology: A descriptive study was conducted on screening program for prepubertal children in primary
schools at Baghdad city, starting from 24th of February to the end of October 2010. Non- probability
(purposive) sample of 510 prepubertal children were chosen from primary schools of both sides of Al-Karkh
and Al-Russafa sectors. Data was collected through a specially constructed questionnaire format include (24)
items multiple choice questions, and
Background: Corona virus disease 2019 (COVID-19) is a communicable disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It was first identified in December 2019 in Wuhan, China, and has since spread globally, leading to an ongoing pandemic.
Aim of study: to review the clinical, lab investigation and imaging techniques, in pediatric age group affected COVID-19 to help medical experts better understand and supply timely diagnosis and treatment.
Subjects and methods: this study is a retrospective descriptive clinical study. The medical records of patients were analyzed. Information’s recorded include demographic data, exposure history, symptoms, signs, laboratory findin
... Show MoreThe present study examines the main points of differences in the subject of greetings between the English language and the Arabic language. From the review of the related literature on greetings in both languages, it is found that Arabic greeting formulas are more elaborate than the English greetings, because of the differences in the social customs and the Arabic traditions and the Arabic culture. It is also found that Arabic greetings carry a religious meaning basing on the Islamic principle of “the same or more so”, which might lead to untranslatable loopholes when rendered in English.
In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial
... Show MoreFive subsurface sections covering the entire length of the Jeribe Limestone Formation (Early Middle Miocene) were studied from four oilfields in northern Iraq. It is hoped to unravel this formation microfacies ; depositional environment; diagenetic attributes and their parental processes; and the relationship between these processes and the observed porosity patterns. The microfacies were found to include mudstone, wackestone, packstone, and grainstone, which have been deposited respectively in open platform, restricted platform, and edge platform which represent the lagoonal environment, while the deposits of the lower parts of the Jeribe formation especially in well Hamrin- 2 reflect a deeper fore slope environment. By using the lithofac
... Show MoreBackground: Lack of durability of the bond of the dental adhesive systems to tooth structure is one of the most important problems in tooth colored restorative work. This in vitro study was performed to evaluate the effect of 2% chlorhexidine gluconate(CHX) on dentin bond strength by using total etch adhesive system at twenty-four hours and three months of water storage. Material and methods:A flat dentin surface was prepared for forty sound human maxillary premolar teeth which were acid etched with 36% phosphoric acid gel after being divided randomly into four groups of ten teeth each according to storage time and CHX application, theCHX was applied for 60 seconds before adhesive application for groups I and III which were tested after twe
... Show MoreWhen optimizing the performance of neural network-based chatbots, determining the optimizer is one of the most important aspects. Optimizers primarily control the adjustment of model parameters such as weight and bias to minimize a loss function during training. Adaptive optimizers such as ADAM have become a standard choice and are widely used for their invariant parameter updates' magnitudes concerning gradient scale variations, but often pose generalization problems. Alternatively, Stochastic Gradient Descent (SGD) with Momentum and the extension of ADAM, the ADAMW, offers several advantages. This study aims to compare and examine the effects of these optimizers on the chatbot CST dataset. The effectiveness of each optimizer is evaluat
... Show MoreChronic liver disease (CLD) can potentially cause disruptions in the normal functioning of various endocrine organs responsible for producing hormones. As a result, individuals suffering from CLD may experience fluctuations or imbalances in the levels of certain hormones within their bodies. As well as they frequently have suppressed immune systems making them more vulnerable to parasite infections. The primary objective of this study was to investigate the association between Toxoplasma gondii infections and liver function by analyzing the interplay between these parasites and hormones. This study was conducted in Baghdad, Iraq from December 2021 to May 2022. One hundred and twenty male patients with Chronic liver disease (CLD) (ag
... Show MoreStart your abstract here the objective of this paper is to study the dynamical behaviour of an eco-epidemiological system. A prey-predator model involving infectious disease with refuge for prey population only, the (SI_) infectious disease is transmitted directly, within the prey species from external sources of the environment as well as, through direct contact between susceptible and infected individuals. Linear type of incidence rate is used to describe the transmission of infectious disease. While Holling type II of functional responses are adopted to describe the predation process of the susceptible and infected predator respectively. This model is represented mathematically by