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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
Tue Mar 28 2017
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
Comparison between Rowatinex and Tamsulosin as a Medical Expulsion Therapy for Ureteral Stone
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The objective of this study is to evaluate the efficacy and safety of rowatinex and tamsulosin in the treatment of patients with ureteric stone.

Forty patients with ureteric stone ranged (4- 12) mm, were included in this study. They were randomized into two groups where the first group includes twenty patients treated with Rowatinex three times daily (Group 1), and the second group includes twenty patients treated with tamsulosin 0.4mg/day (Group 2). All patients were randomly assigned to receive the designed standard medical therapy for a maximum of 3 weeks.

Each group was given an antibiotic as prophylaxis and an injectable non-steroidal anti-inflammatory drug used on demand. At the outpatient clinic all subjects were a

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Publication Date
Tue Jul 01 2014
Journal Name
Journal Of The Faculty Of Medicine Baghdad
Clinical Types and associated maternal factors of Attention Deficit /Hyperactivity Disorder ADHD in a group of children in Baghdad
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Background: Attention Deficit Hyperactivity Disorder (ADHD) is a common neurobehavioral disorders in children includes pervasive inattention, over activity, and impulsivity imposing psychological and cognitive impairment.

Objectives: This study aims to examine the clinical types and the association of some maternal risk factors and developmental milestone with ADHD

Fac Med Baghdad

2014; Vol.56, No.2

Received: Feb., 2014

Accepted April. 2014

 

 
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Publication Date
Wed Dec 15 2021
Journal Name
Iraqi Journal Of Science
Study the Effect of some Medical Plants in Biofilm Formation and Antibiotic Sensitivity for Klebsiella Pneumoniae
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Twenty clinical and fecal samples (ten clinical samples from patients attending to
Imam Ali Hospital and ten chicken faeces samples collected from local poultry farm
in Baghdad city) collected during December 2015, for isolated Klebsiella
pneumoniae. All K. pneumoniae isolates were extended-spectrum β- lactamase
producers and biofilm formation. The activities of two selected K. pneumoniae
isolates for their biofilm formation and susceptibility to antibiotics after treatment
with several plants extracts were investigated. The results of water and 60% ethanol
extracts for Matricaria chamomile flowers, Alhagi maurorum leafs, Syzygium
aromaticum buds (clove) and Arctium minus leafs were showed reduction of biofilm

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Publication Date
Mon Jun 19 2023
Journal Name
Journal Of Engineering
Medical waste management in Al-Kut City
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This research investigates solid waste management in Al-Kut City. It included the collection of medical and general solid waste generated in five hospitals different in their specialization and capacity through one week, starting from 03/02/2012. Samples were  collected and analyzed periodically to find their generation rate, composition, and physical properties. Analysis results indicated that generation rate ranged between (1102 – 212) kg / bed / day, moisture content and density were (19.0 % - 197 kg/ m3) respectively for medical waste and (41%-255 kg/ m3) respectively for general waste. Theoretically, medical solid waste generated in Al-Kut City (like any other city), affected by capacity, number of patients in a day, and hosp

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Publication Date
Thu Jan 11 2018
Journal Name
Al-khwarizmi Engineering Journal
Experimental Estimation of Critical Buckling Velocities for Conservative Pipes Conveying Fluid
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Conservative pipes conveying fluid such as pinned-pinned (p-p), clamped–pinned (c-p) pipes and clamped-clamped (c-c) lose their stability by buckling at certain critical fluid velocities. In order to experimentally evaluate these velocities, high flow-rate pumps that demand complicated fluid circuits must be used.

     This paper studies a new experimental approach based on estimating the critical velocities from the measurement of several fundamental natural frequencies .In this approach low flow-rate pumps and simple fluid circuit can be used.

Experiments were carried out on two pipe models at three different boundary conditions. The results showed that the present approach is more accurate for est

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Publication Date
Wed May 04 2022
Journal Name
Int. J. Nonlinear Anal. Appl.
Knee Meniscus Segmentation and Tear Detection Based On Magnitic Resonacis Images: A Review of Literature
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The meniscus has a crucial function in human anatomy, and Magnetic Resonance Imaging (M.R.I.) plays an essential role in meniscus assessment. It is difficult to identify cartilage lesions using typical image processing approaches because the M.R.I. data is so diverse. An M.R.I. data sequence comprises numerous images, and the attributes area we are searching for may differ from each image in the series. Therefore, feature extraction gets more complicated, hence specifically, traditional image processing becomes very complex. In traditional image processing, a human tells a computer what should be there, but a deep learning (D.L.) algorithm extracts the features of what is already there automatically. The surface changes become valuable when

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Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
Hybrid CNN-based Recommendation System
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Recommendation systems are now being used to address the problem of excess information in several sectors such as entertainment, social networking, and e-commerce. Although conventional methods to recommendation systems have achieved significant success in providing item suggestions, they still face many challenges, including the cold start problem and data sparsity. Numerous recommendation models have been created in order to address these difficulties. Nevertheless, including user or item-specific information has the potential to enhance the performance of recommendations. The ConvFM model is a novel convolutional neural network architecture that combines the capabilities of deep learning for feature extraction with the effectiveness o

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Publication Date
Sun Oct 31 2021
Journal Name
Eastern-european Journal Of Enterprise Technologies
Distinguishing of different tissue types using K-Means clustering of color segmentation
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Millions of lives might be saved if stained tissues could be detected quickly. Image classification algorithms may be used to detect the shape of cancerous cells, which is crucial in determining the severity of the disease. With the rapid advancement of digital technology, digital images now play a critical role in the current day, with rapid applications in the medical and visualization fields. Tissue segmentation in whole-slide photographs is a crucial task in digital pathology, as it is necessary for fast and accurate computer-aided diagnoses. When a tissue picture is stained with eosin and hematoxylin, precise tissue segmentation is especially important for a successful diagnosis. This kind of staining aids pathologists in disti

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Publication Date
Sat Oct 30 2021
Journal Name
Iraqi Journal Of Science
Small Binary Codebook Design for Image Compression Depending on Rotating Blocks
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     The searching process using a binary codebook of combined Block Truncation Coding (BTC) method and Vector Quantization (VQ), i.e. a full codebook search for each input image vector to find the best matched code word in the codebook, requires a long time.   Therefore, in this paper, after designing a small binary codebook, we adopted a new method by rotating each binary code word in this codebook into 900 to 2700 step 900 directions. Then, we systematized each code word depending on its angle  to involve four types of binary code books (i.e. Pour when , Flat when  , Vertical when, or Zigzag). The proposed scheme was used for decreasing the time of the coding pro

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Publication Date
Tue Aug 01 2023
Journal Name
Baghdad Science Journal
A New Model Design for Combating COVID -19 Pandemic Based on SVM and CNN Approaches
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       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

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