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  • 1.
    Abd-Ellah, Mahmoud Khaled
    et al.
    Electronics and Communications Department, Al-Madina Higher Institute for Engineering and Technology, Giza, Egypt.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Faculty of Engineering, Al-Azhar University, Qena, Egypt.
    Khalaf, Ashraf A.M.
    Electronics and Communications Department, Faculty of Engineering, Minia University, Minia, Egypt.
    Hamed, Hesham F.A.
    Electronics and Communications Department, Faculty of Engineering, Minia University, Minia, Egypt.
    A Review on Brain Tumor Diagnosis from MRI Images: Practical Implications, Key Achievements, and Lessons Learned2019In: Magnetic Resonance Imaging, ISSN 0730-725X, E-ISSN 1873-5894, Vol. 61, p. 300-318Article in journal (Refereed)
    Abstract [en]

    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.

  • 2.
    Filippov, Andrei
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Sustainable Process Engineering.
    Dvinskikh, Sergey V.
    Royal Institute of Technology.
    Khakimov, Aidar
    Kazan (Volga Region) Federal University, Kazan.
    Grahn, Mattias
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Sustainable Process Engineering.
    Zhou, Han
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Sustainable Process Engineering.
    Furo, Istvan
    Royal Institute of Technology.
    Antzutkin, Oleg
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Sustainable Process Engineering.
    Hedlund, Jonas
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Sustainable Process Engineering.
    Dynamic properties of water in silicalite-1 powder2012In: Magnetic Resonance Imaging, ISSN 0730-725X, E-ISSN 1873-5894, Vol. 30, no 7, p. 1022-1031Article in journal (Refereed)
    Abstract [en]

    Self-diffusion of D 2O in partially filled silicalite-1 crystals was studied at 25°C by 2H nuclear magnetic resonance (NMR) with bipolar field gradient pulses and longitudinal Eddy-current-delay. For the first time, reliable experimental diffusion data for this system were obtained. Analysis of NMR diffusion decays revealed the presence of a continuous distribution of apparent self-diffusion coefficients (SDCs) of water, ranging from 10 -7 to ~10 -10 m 2/s, which include values much higher and lower than that of bulk water (~10 -9 m 2/s) in liquid phase. The observed distribution of SDC changes with variation of the diffusion time in the range of 10-200 ms. A two-site Kärger exchange model was successfully fitted to the data. Finally, the water distribution and exchange in silicalite-1 pores were described by taking into account (a) a gas-like phase in the zeolite pores, a gas-like phase in mesopores and an intercrystalline gas-like phase and (b) intercrystalline liquid droplets with intermediate exchange rate with the other phases. The other phases experience fast exchange on the NMR diffusion time scale. Diffusion coefficients and mean residence times of water in some of these states were estimated

  • 3.
    Filippov, Andrei
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Sustainable Process Engineering.
    Munavirov, Bulat
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Sustainable Process Engineering.
    Gröbner, Gerhard
    Umeå universitet.
    Rudakova, Maya
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Sustainable Process Engineering.
    Lateral diffusion in equimolar mixtures of natural sphingomyelins with dioleoylphosphatidylcholine2012In: Magnetic Resonance Imaging, ISSN 0730-725X, E-ISSN 1873-5894, Vol. 30, no 3, p. 412-421Article in journal (Refereed)
    Abstract [en]

    Cellular membranes of mammals are composed of a complex assembly of diverse phospholipids. Sphingomyelin (SM) and phosphatidylcholine (PC) are important lipids of eukaryotic cellular membranes and neuronal tissues, and presumably participate in the formation of membrane domains, known as “rafts,” through intermolecular interaction and lateral microphase decomposition. In these two-dimensional membrane systems, lateral diffusion of lipids is an essential dynamic factor, which might even be indicative of lipid phase separation process. Here, we used pulsed field gradient nuclear magnetic resonance to study lateral diffusion of lipid components in macroscopically oriented bilayers composed of equimolar mixtures of natural SMs of egg yolk, bovine brain, bovine milk and dipalmitoylphosphatidylcholine (DPPC) with dioleoylphosphatidylcholine (DOPC). In addition, differential scanning calorimetry was used as a complementary technique to characterize the phase state of the lipid bilayers. In fully liquid bilayers, the lateral diffusion coefficients in both DOPC/DPPC and DOPC/SM systems exhibit mean values of the pure bilayers. For DOPC/SM bilayer system, this behavior can be explained by a model where most SM molecules form short-lived lateral domains with preferential SM–SM interactions occurring within them. However, for bilayers in the presence of their low-temperature gel phase, lateral diffusion becomes complicated and cannot simply be understood solely by a simple change in the liquid phase decomposition.

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