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Glaucoma Diagnosis Based on Retinal Fundus Image: A Review
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    Glaucoma is one of the most dangerous eye diseases. It occurs as a result of an imbalance in the drainage and flow of the retinal fluid. Consequently, intraocular pressure is generated, which is a significant risk factor for glaucoma. Intraocular pressure causes progressive damage to the optic nerve head, thus leading to vision loss in the advanced stages. Glaucoma does not give any signs of disease in the early stages, so it is called "the Silent Thief of Sight". Therefore, early diagnosis and treatment of retinal eye disease is extremely important to prevent vision loss. Many articles aim to analyze fundus retinal images and diagnose glaucoma. This review can be used as a guideline to help diagnose glaucoma. It presents 63 articles related to the applications of fundus retinal analysis. Applications of the glaucomatous image classification are improving fundus images by locating and segmenting the optic disc, optic cup, fovea, and blood vessels. The study also presents datasets, metrics, and parameters that indicate the changes in retina structure and the steps and results for each paper.

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Publication Date
Thu Aug 10 2023
Journal Name
Central Asian Journal Of Medical And Natural Sciences
Role of Gamma Camera Components in Radiological Diagnosis
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The gamma camera, along with SPECT and PET scanners, is one of the main imaging technologies in nuclear medicine. A collimator is typically constructed from tungsten to provide high absorption of gamma photon energies. It has a hole or holes for imaging. Gamma rays from a radioactive source within the body are emitted in all directions, while the photons required constructing an image travel through the hole. A scintillator is the most common material used to convert the high energy of gamma radiation into a lowenergy optical photon. These detectors are one of the primary secrets to radio-diagnosis in nuclear medicine. The photomultiplier tube (PMT) is a versatile device with extraordinarily highly sensitivity and response. A typical photom

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Publication Date
Sat Oct 01 2022
Journal Name
Baghdad Science Journal
COVID-19 Diagnosis System using SimpNet Deep Model
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After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings

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Publication Date
Mon Jan 01 2024
Journal Name
Pathology - Research And Practice
Artificial intelligence in cancer diagnosis: Opportunities and challenges
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Publication Date
Thu Jun 30 2022
Journal Name
Iraqi Journal Of Science
Spectroscopic Diagnosis of Arc Carbon and Magnesium Plasma
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      This research aims to investigate parameters for magnesium (Mg)  carbon (C), and carbon/magnesium plasma produced by the exploding electrical wire (EEW) technique. In this work, C and Mg nanoparticles were synthesized. The plasma spectra with three different current values (50, 75 and 100A) were recorded using optical emission spectroscopy (OES). The plasma electron temperature (Te), electron density (ne), plasma frequency (fp), Debye length (ℷD), and Debye number (ND) provided by arc discharge plasma were  calculated. Boltzmann plots were  used to calculate the electron temperature (Te); electron density (ne) was calculated by Stark broadening . The results showed that the electron temperature and electron density increa

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Publication Date
Sat Apr 01 2023
Journal Name
Heliyon
A comprehensive review on modelling the adsorption process for heavy metal removal from waste water using artificial neural network technique
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Publication Date
Wed Apr 05 2023
Journal Name
International Journal Of Interactive Mobile Technologies (ijim)
A Partial Face Encryption in Real World Experiences Based on Features Extraction from Edge Detection
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User confidentiality protection is concerning a topic in control and monitoring spaces. In image, user's faces security in concerning with compound information, abused situations, participation on global transmission media and real-world experiences are extremely significant. For minifying the counting needs for vast size of image info and for minifying the size of time needful for the image to be address computationally. consequently, partial encryption user-face is picked. This study focuses on a large technique that is designed to encrypt the user's face slightly. Primarily, dlib is utilizing for user-face detection. Susan is one of the top edge detectors with valuable localization characteristics marked edges, is used to extract

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Publication Date
Sun Dec 27 2020
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
Analysis of Docetaxel Adverse Drug Reactions: A Retrospective Study Based on Iraqi Pharmacovigilance Center Database
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Docetaxel is an effective treatment approved for many types of cancers, but its effectiveness in clinical practice can be compromised by significant occurrence of adverse drug reactions. The aim of the current study was to measure the distribution of adverse drug reactions of docetaxel reported in Iraq and to assess the causality, severity, seriousness, preventability, expectedness and outcome of these adverse reactions. A retrospective study conducted on individual case safety reports from the Iraqi Pharmacovigilance Center / Ministry of Health. The study included 118 individual case safety report containing 236 adverse drug reactions.
Most of the adverse drug reactions were related to skin and subcutaneous tissue disorders(26.7%), f

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Publication Date
Sat Feb 25 2017
Journal Name
International Journal On Advanced Science, Engineering And Information Technology
A Novel DNA Sequence Approach for Network Intrusion Detection System Based on Cryptography Encoding Method
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A novel method for Network Intrusion Detection System (NIDS) has been proposed, based on the concept of how DNA sequence detects disease as both domains have similar conceptual method of detection. Three important steps have been proposed to apply DNA sequence for NIDS: convert the network traffic data into a form of DNA sequence using Cryptography encoding method; discover patterns of Short Tandem Repeats (STR) sequence for each network traffic attack using Teiresias algorithm; and conduct classification process depends upon STR sequence based on Horspool algorithm. 10% KDD Cup 1999 data set is used for training phase. Correct KDD Cup 1999 data set is used for testing phase to evaluate the proposed method. The current experiment results sh

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Publication Date
Thu Sep 30 2021
Journal Name
Iraqi Journal Of Science
A Proposed Algorithm for Encrypted Data Hiding in Video Stream Based on Frame Random Distribution
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     The science of information security has become a concern of many researchers, whose efforts are trying to come up with solutions and technologies that ensure the transfer of information in a more secure manner through the network, especially the Internet, without any penetration of that information, given the risk of digital data being sent between the two parties through an insecure channel. This paper includes two data protection techniques. The first technique is cryptography by using Menezes Vanstone elliptic curve ciphering system, which depends on public key technologies. Then, the encoded data is randomly included in the frame, depending on the seed used. The experimental results, using a PSNR within avera

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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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