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Predication of Most Significant Features in Medical Image by Utilized CNN and Heatmap.
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The growth of developments in machine learning, the image processing methods along with availability of the medical imaging data are taking a big increase in the utilization of machine learning strategies in the medical area. The utilization of neural networks, mainly, in recent days, the convolutional neural networks (CNN), have powerful descriptors for computer added diagnosis systems. Even so, there are several issues when work with medical images in which many of medical images possess a low-quality noise-to-signal (NSR) ratio compared to scenes obtained with a digital camera, that generally qualified a confusingly low spatial resolution and tends to make the contrast between different tissues of body are very low and it difficult to computed and recognized dependably. In this paper, we target to utilized CNN and heatmap to recognized most significant features that the network should focus on it. depending on class activation mapping. The goal of this study is to develop an approach that can determine the most significant features from medical images (such as x-ray, CT, MRI) through gradient the different tissue accurately by made use of heatmap. In our model, we take the gradient with regard to the final convolutional layer and after that weigh it towards the output of this layer. The model is based upon class activation mapping. However, the model is differed from traditional activation mapping based methods, that this model is the dependent on gradients via obtaining the weight of all activation map via make use of it is forward passing score over target class, then the final result is apart from linear combination of activation and weights. The results appears that the model is successfully distortion heat map of tissues in various medical image techniques and obtained better visual accuracy and fairness for interpretation the decision-making procedure.

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Publication Date
Fri May 01 2020
Journal Name
International Journal Of Advanced Science And Technology
Improved Merging Multi Convolutional Neural Networks Framework of Image Indexing and Retrieval
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Background/Objectives: The purpose of current research aims to a modified image representation framework for Content-Based Image Retrieval (CBIR) through gray scale input image, Zernike Moments (ZMs) properties, Local Binary Pattern (LBP), Y Color Space, Slantlet Transform (SLT), and Discrete Wavelet Transform (DWT). Methods/Statistical analysis: This study surveyed and analysed three standard datasets WANG V1.0, WANG V2.0, and Caltech 101. The features an image of objects in this sets that belong to 101 classes-with approximately 40-800 images for every category. The suggested infrastructure within the study seeks to present a description and operationalization of the CBIR system through automated attribute extraction system premised on CN

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Publication Date
Tue Dec 11 2018
Journal Name
Iraqi National Journal Of Nursing Specialties
Determination of the Impact of Electronic Health Information Systems upon Medical, Medical Backing and Administrative Decisions in Al-Kindy Teaching Hospital
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Objective(s): To determine the impact of the electronic Health Information Systems upon medical, medical Backing and administrativedecisions in Al-Kindy Teaching Hospital. Methodology: A descriptive analytical design is employed through the period of June 14th 2015 to August 15th 2015. A purposive "non- probability" sample of (50) subject is selected. The sample is comprised of (25) medical and medical backing staff and (25) administrative staff who are all involved in the process of decision making in Al-Kindy Teaching Hospital. A self-report questionnaire, of (68) item, is adopted and developed for the purpo

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Publication Date
Sat Jan 01 2022
Journal Name
Journal Of Pharmaceutical Negative Results
Evaluation Of Most Common Microorganisms Associated with Ectopic Pregnancy by Real Time PCR Among Iraqi Women
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Publication Date
Fri Mar 29 2024
Journal Name
Iraqi Journal Of Science
Evaluating the Performance and Behavior of CNN, LSTM, and GRU for Classification and Prediction Tasks
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     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod

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Publication Date
Mon Mar 01 2010
Journal Name
Basrah Journal Of Science
Hiding Three Images at one image by Using Wavelet Coefficients at Color Image
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Publication Date
Mon Nov 16 2020
Journal Name
The Imaging Science Journal
Single image dehazing by dark channel prior and luminance adjustment
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Publication Date
Fri Apr 30 2021
Journal Name
Al-kindy College Medical Journal
The Role of MRI-US Fusion Techniques in Detection of Clinically Significant Prostate Cancer
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Prostate cancer is the commonest male cancer and the second leading cause of cancer-related death in men. Over many decades, prostate cancer detection represented a continuous challenge to urologists. Although all urologists and pathologists agree that tissue diagnosis is essential especially before commencing active surgical or radiation treatment, the best way to obtain the biopsy was always the big hurdle. The heterogenicity of the tumor pathology is very well seen in its radiological appearance. Ultrasound has been proven to be of limited sensitivity and specificity in detecting prostate cancer. However, it was the only available targeting technique for years and was used to guide biopsy needle passed transrectally or transperineally

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Publication Date
Wed Jun 30 2021
Journal Name
College Of Islamic Sciences
العربية Atheism A study in the most important foundations of atheism, and the most prominent suspicions of its advocates: دراسة في أهمّ مرتكزاته، وأبرز شبهات دُعاته
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Atheism is one of the most dangerous absurd doctrines that have swept the world in the modern era. Its strength lies in the bodies that stand behind it and adopt its ideologies which are accompanied by the tremendous media momentum of the theses of its supporters based on scientific and non-scientific levels. And since the denial of the existence of the Creator is the main focus and central point which the atheists based their view towards the universe, life and man, this humble research came to discuss the claim of the existence of the world by pure chance, and try to explain this scientifically through the theory of natural selection and survival of the fittest developed by the English biologist Charles Darwin. The resea

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Publication Date
Sun Dec 31 2023
Journal Name
College Of Islamic Sciences
The most prominent philosophical and rhetorical concepts in the perspective of philosophers and theologians
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     This research aims to clarify and define the most important philosophical and rhetorical concepts to which many philosophical and rhetorical issues refer، since they have an effective role in the diversity and difference of intellectual schools، which are indispensable in proving major dogmatic issues، such as the concept of (existence، being، essence، and authenticity). Existence or Essence in contrast to the consideration of the other concept)، because it is one of the complex concepts and common words that carry different connotations among thinkers.

   And from (the rule of judging something is a branch of its perception)، the researchers began to define these concepts، as evidence of the sincerity of perce

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Publication Date
Wed Dec 12 2018
Journal Name
Iraqi National Journal Of Nursing Specialties
Determination of the Impact of Electronic Health Information Systems upon Medical, Medical Backing and Administrative Business Fields in Al-Kindy Teaching Hospital
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Objective(s): To determine the impact of the Electronic Health Information Systems upon medical, medical backing and administrative business fields in Al-Kindy Teaching Hospital and to identify the relationship between such impact and their demographic characteristics of years of employment, place of work, and education. Methodology: A descriptive analytical design is employed through the period of April 25th 2016 to May 28th 2016. A purposive "non- probability" sample of (50) subject is selected. The sample is comprised of (25) medical and medical backing staff and (25) administrative staff who are all

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