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Träfflista för sökning "WFRF:(Hamed Hesham F. A.) "

Sökning: WFRF:(Hamed Hesham F. A.)

  • Resultat 1-10 av 11
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  • Burstein, Roy, et al. (författare)
  • Mapping 123 million neonatal, infant and child deaths between 2000 and 2017
  • 2019
  • Ingår i: Nature. - 0028-0836 .- 1476-4687. ; 574:7778, s. 353-358
  • Tidskriftsartikel (refereegranskat)abstract
    • <p>Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2-to end preventable child deaths by 2030-we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000-2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations.</p>
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  • Abd-Ellah, Mahmoud Khaled, et al. (författare)
  • A Review on Brain Tumor Diagnosis from MRI Images : Practical Implications, Key Achievements, and Lessons Learned
  • 2019
  • Ingår i: Magnetic Resonance Imaging. - Elsevier. - 0730-725X .- 1873-5894. ; 61, s. 300-318
  • Tidskriftsartikel (refereegranskat)abstract
    • <p>The successful early diagnosis of brain tumors plays a major role in improving the treatment outcomes and thus improving patient survival. Manually evaluating the numerous magnetic resonance imaging (MRI) images produced routinely in the clinic is a difficult process. Thus, there is a crucial need for computer-aided methods with better accuracy for early tumor diagnosis. Computer-aided brain tumor diagnosis from MRI images consists of tumor detection, segmentation, and classification processes. Over the past few years, many studies have focused on traditional or classical machine learning techniques for brain tumor diagnosis. Recently, interest has developed in using deep learning techniques for diagnosing brain tumors with better accuracy and robustness. This study presents a comprehensive review of traditional machine learning techniques and evolving deep learning techniques for brain tumor diagnosis. This review paper identifies the key achievements reflected in the performance measurement metrics of the applied algorithms in the three diagnosis processes. In addition, this study discusses the key findings and draws attention to the lessons learned as a roadmap for future research.</p>
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4.
  • Abd-Ellah, Mahmoud Khaled, et al. (författare)
  • Classification of Brain Tumor MRIs Using a Kernel Support Vector Machine
  • 2016
  • Ingår i: Building Sustainable Health Ecosystems : 6th International Conference on Well-Being in the Information Society, WIS 2016, Tampere, Finland, September 16-18, 2016, Proceedings. - Springer International Publishing. - 978-3-319-44671-4 - 978-3-319-44672-1 ; s. 151-160
  • Konferensbidrag (refereegranskat)abstract
    • <p>The use of medical images has been continuously increasing, which makes manual investigations of every image a difficult task. This study focuses on classifying brain magnetic resonance images (MRIs) as normal, where a brain tumor is absent, or as abnormal, where a brain tumor is present. A hybrid intelligent system for automatic brain tumor detection and MRI classification is proposed. This system assists radiologists in interpreting the MRIs, improves the brain tumor diagnostic accuracy, and directs the focus toward the abnormal images only. The proposed computer-aided diagnosis (CAD) system consists of five steps: MRI preprocessing to remove the background noise, image segmentation by combining Otsu binarization and K-means clustering, feature extraction using the discrete wavelet transform (DWT) approach, and dimensionality reduction of the features by applying the principal component analysis (PCA) method. The major features were submitted to a kernel support vector machine (KSVM) for performing the MRI classification. The performance evaluation of the proposed system measured a maximum classification accuracy of 100 % using an available MRIs database. The processing time for all processes was recorded as 1.23 seconds. The obtained results have demonstrated the superiority of the proposed system.</p>
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  • Abd-Ellah, Mahmoud Khaled, et al. (författare)
  • Design and implementation of a computer-aided diagnosis system for brain tumor classification
  • 2017
  • Ingår i: 2016 28th International Conference on Microelectronics (ICM). - 978-1-5090-5721-4 ; s. 73-76
  • Konferensbidrag (refereegranskat)abstract
    • <p>Computer-aided diagnosis (CAD) systems have become very important for the medical diagnosis of brain tumors. The systems improve the diagnostic accuracy and reduce the required time. In this paper, a two-stage CAD system has been developed for automatic detection and classification of brain tumor through magnetic resonance images (MRIs). In the first stage, the system classifies brain tumor MRI into normal and abnormal images. In the second stage, the type of tumor is classified as benign (Noncancerous) or malignant (Cancerous) from the abnormal MRIs. The proposed CAD ensembles the following computational methods: MRI image segmentation by K-means clustering, feature extraction using discrete wavelet transform (DWT), feature reduction by applying principal component analysis (PCA). The two-stage classification has been conducted using a support vector machine (SVM). Performance evaluation of the proposed CAD has achieved promising results using a non-standard MRIs database.</p>
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7.
  • Abd-Ellah, Mahmoud Khaled, et al. (författare)
  • Parallel Deep CNN Structure for Glioma Detection and Classification via Brain MRI Images
  • 2019
  • Ingår i: IEEE-ICM 2019 CAIRO-EGYPT : The 31st International Conference on Microelectronics. - IEEE. ; s. 304-307
  • Konferensbidrag (övrigt vetenskapligt)abstract
    • <p>Although most brain tumor diagnosis studies have focused on tumor segmentation and localization operations, few studies have focused on tumor detection as a time- and effort-saving process. This study introduces a new network structure for accurate glioma tumor detection and classification using two parallel deep convolutional neural networks (PDCNNs). The proposed structure is designed to identify the presence and absence of a brain tumor in MRI images and classify the type of tumor images as high-grade gliomas (HGGs, i.e., glioblastomas) or low-grade gliomas (LGGs). The introduced PDCNNs structure takes advantage of both global and local features extracted from the two parallel stages. The proposed structure is not only accurate but also efficient, as the convolutional layers are more accurate because they learn spatial features, and they are efficient in the testing phase since they reduce the number of weights, which reduces the memory usage and runtime. Simulation experiments were accomplished using an MRI dataset extracted from the BraTS 2017 database. The obtained results show that the proposed parallel network structure outperforms other detection and classification methods in the literature.</p>
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8.
  • Khaled Abd-Ellah, Mahmoud, et al. (författare)
  • Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks
  • 2018
  • Ingår i: EURASIP Journal on Image and Video Processing. - Springer. - 1687-5176 .- 1687-5281. ; 2018
  • Tidskriftsartikel (refereegranskat)abstract
    • <p>Brain tumour is a serious disease, and the number of people who are dying due to brain tumours is increasing. Manual tumour diagnosis from magnetic resonance images (MRIs) is a time consuming process and is insufficient for accurately detecting, localizing, and classifying the tumour type. This research proposes a novel two-phase multi-model automatic diagnosis system for brain tumour detection and localization. In the first phase, the system structure consists of preprocessing, feature extraction using a convolutional neural network (CNN), and feature classification using the error-correcting output codes support vector machine (ECOC-SVM) approach. The purpose of the first system phase is to detect brain tumour by classifying the MRIs into normal and abnormal images. The aim of the second system phase is to localize the tumour within the abnormal MRIs using a fully designed five-layer region-based convolutional neural network (R-CNN). The performance of the first phase was assessed using three CNN models, namely, AlexNet, Visual Geometry Group (VGG)-16, and VGG-19, and a maximum detection accuracy of 99.55% was achieved with AlexNet using 349 images extracted from the standard Reference Image Database to Evaluate Response (RIDER) Neuro MRI database. The brain tumour localization phase was evaluated using 804 3D MRIs from the Brain Tumor Segmentation (BraTS) 2013 database, and a DICE score of 0.87 was achieved. The empirical work proved the outstanding performance of the proposed deep learning-based system in tumour detection compared to other non-deep-learning approaches in the literature. The obtained results also demonstrate the superiority of the proposed system concerning both tumour detection and localization.</p>
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9.
  • Mamdouh, Moustafa, et al. (författare)
  • Outlook on Security and Privacy in IoHT : Key Challenges and Future Vision
  • 2020
  • Ingår i: Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). - Springer. - 978-3-030-44288-0 - 978-3-030-44289-7 ; s. 721-730
  • Konferensbidrag (övrigt vetenskapligt)abstract
    • <p>The Internet of Things (IoT) security and privacy have received considerable research attention due to the IoT applicability in various domains. IoT systems have several applications, such as smart homes, smart cities, e-Health, industry, agriculture, and environmental monitoring. One of the most important applications is the Internet of Healthcare Things (IoHT) because it helps humans and patients obtain rapid diagnoses, remote monitoring, and home rehabilitation. IoHT security can be classified into four categories: applications, architecture, communication, and data security. This paper presents a short, but focused, review on IoHT security and privacy. It also explores recent security algorithms and protocols that are used to secure personal patient data, clinicians, and healthcare information. The future vision of IoHT challenges and countermeasures is given at the end of this study. Blockchain healthcare technology provides secure digital payment and privileged data access.</p>
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10.
  • Elrawy, Mohammed Faisal, et al. (författare)
  • Flow-based Features for a Robust Intrusion Detection System Targeting Mobile Traffic
  • 2016
  • Ingår i: 23rd International Conference on Telecommunications (ICT) : Thessaloniki, 16-18 May 2016. - Piscataway, NJ : IEEE Communications Society. - 9781509019908
  • Konferensbidrag (refereegranskat)abstract
    • <p>The security risks and threats that impact wired and wireless networks are now applicable to mobile telecommunication networks. Threat detection systems should be more intelligent because threats are becoming more dangerous. An intrusion detection system (IDS) is a potential network security solution for protecting the confidentiality, integrity, and availability of user data and information resources. A fast and effective IDS for mobile networks that does not violate the user's privacy or the network's QoS is required. This paper offers a set of flow-based features that can be utilized for mobile network traffic as a prerequisite for a privacy-aware and QoS-robust IDS. The principal component analysis (PCA) method was used for reduction of the features. Twelve features in six groups, which represent the user data in mobile traffic, were extracted and evaluated for IDSs. The evaluation process achieved a F-measure weighted average equal to 0.834, and the experimental time was equal to 12.9 seconds. The accomplished measurements have demonstrated the applicability of the proposed set of features.</p>
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