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MSRD-Unet: Multiscale Residual Dilated U-Net for Medical Image Segmentation
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Semantic segmentation is an exciting research topic in medical image analysis because it aims to detect objects in medical images. In recent years, approaches based on deep learning have shown a more reliable performance than traditional approaches in medical image segmentation. The U-Net network is one of the most successful end-to-end convolutional neural networks (CNNs) presented for medical image segmentation. This paper proposes a multiscale Residual Dilated convolution neural network (MSRD-UNet) based on U-Net. MSRD-UNet replaced the traditional convolution block with a novel deeper block that fuses multi-layer features using dilated and residual convolution. In addition, the squeeze and execution attention mechanism (SE) and the skip connections are redesigned to give a more reliable fusion of features. MSRD-UNet allows aggregation of contextual information, and the network goes without needing to increase the number of parameters or required floating-point operations (FLOPS). The proposed model was evaluated on three multimodal datasets: polyp, skin lesion, and nuclei segmentation. The obtained results proved that the MSDR-Unet model outperforms several state-of-the-art U-Net-based methods.

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Publication Date
Mon Oct 01 2018
Journal Name
Journal Of Economics And Administrative Sciences
Manegment of the medical waste in the kindy hospital
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The problem of medical waste over the past tow decades has emerged as one of the most important issues.

Which have negative effects on health and the environment ,and as a result specialists looked into the field.

Public health and research issues . This phenomenon in all its dimensions and efforts made For their containment through the development of health and environmental controls for the disposal of such wastes.

In a safe manner starting form the source of these wastes and the various health organizations are  finished The final treatment ,and this is why the producers of hazardous medical waste.        &nb

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Publication Date
Tue Jun 20 2023
Journal Name
Baghdad Science Journal
Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network
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Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D

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Publication Date
Mon Feb 04 2019
Journal Name
Journal Of The College Of Education For Women
Classification of Rural Road Network in Al-Najaf Governorate
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This study has dealt with, the issue of classification of rural road network , in addition to prepare a suggested for the classification for this network in Iraq , this classification account , the specifications and characteristics of rural roads, population, and the range taking of settlements , then this classification was applied on the rural road network in the Najaf province there are four categories of classification ,the first is major arterial rural roads divided into two major arterial and minor arterial roads , while the second category collected roads which was divided into minor arterial roads and main collected roads. The third category was represented by Local Roads , it has been divided into paved roads and unpaved, the f

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Publication Date
Fri Aug 31 2012
Journal Name
Al-khwarizmi Engineering Journal
Design a Security Network System against Internet Worms
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 Active worms have posed a major security threat to the Internet, and many research efforts have focused on them. This paper is interested in internet worm that spreads via TCP, which accounts for the majority of internet traffic. It presents an approach that use a hybrid solution between two detection algorithms: behavior base detection and signature base detection to have the features of each of them. The aim of this study is to have a good solution of detecting worm and stealthy worm with the feature of the speed. This proposal was designed in distributed collaborative scheme based on the small-world network model to effectively improve the system performance.

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Publication Date
Fri Jun 01 2018
Journal Name
Journal Of Legal Sciences
Compensation for Victims of Technological Risks in the Scope of Medical Practice
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those affected by technological development risks within the scope of medical works. The unprecedented technological development the world witnesses nowadays has been providing brilliant medical service to the human being including examination, diagnosis and the treatment or follow up. However, such works hide behind potential risks threatening people's lives and such risks my be discovered within the limits of now – how and technical knowledge prevailing the time of rendering the medical service. Also this the question is raised on how to keep up between the safety of the patients and such risks are being unknown by the provider and questioning them contradicts the justices. subsequently, can the patients (affected) acquire the compensat

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Publication Date
Fri Jan 01 2016
Journal Name
Bio-inspired Computing – Theories And Applications
Image Segmentation Using Membrane Computing: A Literature Survey
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Publication Date
Sun Jul 09 2023
Journal Name
Journal Of Engineering
MR Brain Image Segmentation Using Spatial Fuzzy C- Means Clustering Algorithm
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conventional FCM algorithm does not fully utilize the spatial information in the image. In this research, we use a FCM algorithm that incorporates spatial information into the membership function for clustering. The spatial function is the summation of the membership functions in the neighborhood of each pixel under consideration. The advantages of the method are that it is less
sensitive to noise than other techniques, and it yields regions more homogeneous than those of other methods. This technique is a powerful method for noisy image segmentation. 

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Publication Date
Thu Mar 03 2022
Journal Name
Multimedia Tools And Applications
Boosting Marine Predators Algorithm by Salp Swarm Algorithm for Multilevel Thresholding Image Segmentation
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Publication Date
Mon Jun 01 2020
Journal Name
Journal Of Planner And Development
The Quantitative Analysis To Assess The Efficiency Of The Transport Network In Sader City
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     This research examines the quantitative analysis to assess the efficiency of the transport network in Sadr City, where the study area suffers from a large traffic movement  for the variability of traffic flow and intensity at peak hours as a result of inside traffic and outside of it, especially in the neighborhoods of population with  economic concentration.                                                           &n

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Publication Date
Fri Dec 31 2021
Journal Name
Iraqi Journal Of Market Research And Consumer Protection
ULATRAVIOLET ABSORBANCE SEPECTRA FOR ANTIBIOTIC DERIVATIVES (AMINO GLYCOSIDES) USED IN MEDICAL AND PHARMACEUTICAL INDUSTRY: ULATRAVIOLET ABSORBANCE SEPECTRA FOR ANTIBIOTIC DERIVATIVES (AMINO GLYCOSIDES) USED IN MEDICAL AND PHARMACEUTICAL INDUSTRY
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Ultraviolet spectrophotometric studies for antibiotic (amino glycoside) derivatives including, Neomycin, Streptomycin, Gentamycin and Kanamycin with special reagents, which are benzoyl chloride; benzene sulfonyl chloride, toluenesulfonyl chloride and phthalic anhydride were made. Amino glycosides derivatives were followed through measurements of the ultraviolet absorbance (A) from which the absorptivity (ε) of the complexes was deduced and molar absorbances using Ultraviolet for products and calculate the number of reagents molecule that combine to amino glycosides.

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