Deep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for Chest X-Ray Analysis of Lung Cancer

The recent progress of computing, machine learning, and especially deep learning, for image recognition brings a meaningful effect for automatic detection of various diseases from chest X-ray images (CXRs). Here efficiency of lung segmentation and bone shadow exclusion techniques is demonstrated for analysis of 2D CXRs by deep learning approach to help radiologists identify suspicious lesions and nodules in lung cancer patients. Training and validation was performed on the original JSRT dataset (dataset #01), BSE-JSRT dataset, i.e. the same JSRT dataset, but without clavicle and rib shadows (dataset #02), original JSRT dataset after segmentation (dataset #03), and BSE-JSRT dataset after segmentation (dataset #04). The results demonstrate the high efficiency and usefulness of the considered pre-processing techniques in the simplified configuration even. The pre-processed dataset without bones (dataset #02) demonstrates the much better accuracy and loss results in comparison to the other pre-processed datasets after lung segmentation (datasets #02 and #03).Comment: 10 pages, 7 figures; The First International Conference on Computer Science, Engineering and Education Applications (ICCSEEA2018) (www.uacnconf.org/iccseea2018) (accepted

Nuclear radiation shielding performance of borosilicate glasses: Numerical simulations and theoretical analyses

The photon shielding performances of five different borosilicate-based glasses were investigated in this study using the FLUKA, GEANT4 and MATLAB codes, as well as the XCOM program, at photon energies ranging from 0.03 to 15 MeV. In this context, dependencies of the photon attenuation features with the variation of the photon energy and the chemical compositions have been carefully evaluated with Monte Carlo simulation and theoretical evaluation tools. The mass attenuation coefficient values and effective atomic numbers obtained for BaO-doped G5 glasses are found to be higher than those derived for G1-G4 samples. In other words, the Zeff results showed that high Z-elements such as Ba in a suitable amount should be inserted into the glass composition in order to improve the photon attenuation capability of the borosilicate glasses. The HVLs, TVLs, and MFPs of the studied borosilicate glasses are determined further, and the gamma shielding characteristics of the analyzed samples are found to be associated to the density of the glass, implying that high-density glass can be used for high-level attenuation performance. The exposure buildup factor (EBF) values have been further estimated via the G-P fitting approach. The results of such investigations, according to the work given, may be valuable in designing and fabricating new borosilicate-based glasses, which can then shield against potential radiation damage to environmental health

A comparative neutron and gamma-ray radiation shielding investigation of molybdenum and boron filled polymer composites

WOS:000990393800001This work presents a detailed radiation shielding study for polymer composites filled with Boron and Molybdenum additives. The chosen novel polymer composites were produced at different percentages of the additive materials to provide a proper evaluation of their neutron and gamma-ray attenuation abilities. The effect of additive particle size on the shielding characteristics was further investigated. On the gamma-ray side, simulation, theoretical and experimental evaluations were performed in a wide range of photon energies varying from 59.5 keV to 1332.5 keV with help of MC simulations (GEANT4 and FLUKA), WinXCOM code, a High Purity Germanium Detector, respectively. A remarkable consistency was reported between them. On the neutron shielding side, the prepared samples produced with nano and micron particle size additives were additionally examined by providing fast neutron removal cross-section (ΣR) and the simulated neutron transmissions through the prepared samples. The samples filled with nano sized particles show better shielding capability than the one filled with micron sized particles. In other words, a new polymer shielding material that does not contain toxic content is introduced: the sample codded N–B0Mo50 exhibits superior radiation attenuation

Gamma and Neutron Shielding Parameters of Polyester-based composites reinforced with boron and tin nanopowders

WOS:000848847800006The usage of composites as the shielding materials are highly recommended since they could be used in order to attenuate the undesired radiation with unique properties and advantages in the areas where the radiation is prevalent. In this context, not only are their radiation shielding properties important but also their flexibility, durability and low cost. Due to the mentioned superior characteristics, the polyester based composites are among the most preferred materials. With the aim of creating unique and novel radiation shielding materials, this study investigates gamma and neutron shielding capabilities of the polyester composites reinforced with Boron and Tin nanopowders at different proportions (0–50%, 10–40%, 20–30%, 30-20% and 40-10%, 50-0%). The gamma shielding abilities of the prepared polyester composite materials were evaluated using an HPGe detector system, WinXCOM computer program and different simulation tools (FLUKA and GEANT4) at the energies varying from 59.5 to 1332.5 keV. The experimental, theoretical and simulation results showed remarkable agreement between each other, and the addition of Sn enhances the gamma attenuation performance of the chosen polyester composite materials. In addition to gamma analysis results, neutron shielding properties of the proposed composites are further determined. On this purpose, the transmitted neutron numbers through the samples (as functions of neutron energy and the sample thickness) and effective neutron removal cross sections were evaluated. The neutron shielding performance of the samples showed that the prepared composites could be alternative materials to the existing neutron shields in the literature. © 2022 Elsevier Ltd