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.
Three Seismic Attributes are used to enhance or delineate geologic feature that cannot be detected within seismic resolution limit. These are Instantaneous Amplitude, Instantaneous Phase and Instantaneous Frequency Attributes. These are applied along two defined picked surface horizons within 3D seismic data for an area in southern Iraq. Two geologic features are deduced, the first represents complex channel system at the top of Saadi Formation and the second represents submarine fan within Mishrif Formation. The semblances of these ancient geological features are dramatically enhanced by using flattening technique.
Among many problems that reduced the performance of the network, especially Wide Area Network, congestion is one of these, which is caused when traffic request reaches or exceeds the available capacity of a route, resulting in blocking and less throughput per unit time. Congestion management attributes try to manage such cases. The work presented in this paper deals with an important issue that is the Quality of Service (QoS) techniques. QoS is the combination effect on service level, which locates the user's degree of contentment of the service. In this paper, packet schedulers (FIFO, WFQ, CQ and PQ) were implemented and evaluated under different applications with different priorities. The results show that WFQ scheduler gives acceptable r
... Show MoreThis research deals with the use of a number of statistical methods, such as the kernel method, watershed, histogram, and cubic spline, to improve the contrast of digital images. The results obtained according to the RSME and NCC standards have proven that the spline method is the most accurate in the results compared to other statistical methods.
Measuring goat weight on a farm is carried out by two methods. The first method conventionally measures the chest girth (CG) and body length (BL), while the second is performed using a scale. This study aimed to facilitate the CG and BL measurements to estimate goat weight more efficiently through image processing. The endpoints of CG and BL were determined automatically using binary large object (BLOB) segmentation. The images of 120 goats were taken at three different shooting distances of 50 cm, 70 cm, and 90 cm. The statistical testing of the three scenarios in this study explained that the ideal distance to take pictures is the basis for determining whether or not the size of the circumference and BL is appropriate as a fea
... Show MoreIn the image processing’s field and computer vision it’s important to represent the image by its information. Image information comes from the image’s features that extracted from it using feature detection/extraction techniques and features description. Features in computer vision define informative data. For human eye its perfect to extract information from raw image, but computer cannot recognize image information. This is why various feature extraction techniques have been presented and progressed rapidly. This paper presents a general overview of the feature extraction categories for image.
Background: The post-operative acute abdominal complication is one of the most difficult clinical problems facing the surgeon, and it represents a unique challenge for him not only because of the difficulty in making a precise diagnosis but also in the decision for further management . Objective: discuss the post-operative acute abdominal complications requiring re-interventionType of the study: Cross sectional study. Methods : Patients with early post-operative Acute Abdominal complications ( within 30 days from the initial operation ) who required re-intervention were studied prospectively Results :The study included 82 patients 47 of them were females, their age ranging 7-87,Different types of the initial operation were reported,51 %
... Show MoreBackground: Laparoscopic cholecystectomy (LC) has become the treatment of choice for elective cholecystectomy.Objectives: To evaluate the safety and feasibility of early LC for AC and to compare the results with delayed LC.Methods: A prospective study done from April 2011 to October 2013, 88 patients with diagnosis of AC were divided randomly into two groups according to the mode of treatment; (early group n=40) treated by early LC within first 72 hours or (delayed group, n=48) initial conservative treatment for 4-6 weeks, followed by delayed LC.Results: There was no difference between the two groups (early & delayed LC), operating time (early 80min, delayed70min), conversion rate (early 7.5%, delayed 6.25%),postoperative complicatio
... Show MoreToxoplasma gondii (Nicolle and Manceaux ) infects all warm-blooded animals,
including humans. Early diagnosis and determining the infective stage are critical for
treating immunosuppressed individuals and pregnant women with toxoplasmosis.
This parasite modulates pro- and anti-inflammatory responses to regulate parasite
multiplication and host survival. The aim of this study was to investigate the
probability of using IL-6 as a marker of toxoplasmosis disease activity (acute and
chronic) in different groups of women (miscarriage, pregnant and single) and estimate
the relationship between infection and gestational age and history of abortion in
miscarriage and pregnant women. The most abortion were occurred at the
In this work, a new development of predictive voltage-tracking control algorithm for Proton Exchange Membrane Fuel Cell (PEMFCs) model, using a neural network technique based on-line auto-tuning intelligent algorithm was proposed. The aim of proposed robust feedback nonlinear neural predictive voltage controller is to find precisely and quickly the optimal hydrogen partial pressure action to control the stack terminal voltage of the (PEMFC) model for N-step ahead prediction. The Chaotic Particle Swarm Optimization (CPSO) implemented as a stable and robust on-line auto-tune algorithm to find the optimal weights for the proposed predictive neural network controller to improve system performance in terms of fast-tracking de
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