A New Joint Detection Algorithm of Conveyer Belt X-Ray Imaging Using the BP Neural Networks

The application of artificial intelligence (AI) such as deep learning in the quality control of grains has the potential to assist analysts in decision making and improving procedures. Advanced technologies based on X-ray imaging provide markedly easier ways to control insect infestation of stored products, regardless of whether the quality features are visible on the surface of the grains. Here, we applied contrast enhancement algorithms based on peripheral equalization and calcification emphasis on X-ray images to improve the detection of Sitophilus zeamais in maize grains. In addition, we proposed an approach based on convolutional neural networks (CNNs) to identity non-infested and infested classes using three different architectures; (i) Inception-ResNet-v2, (ii) Xception and (iii) MobileNetV2. In general, the prediction models developed based on the MobileNetV2 and Xception architectures achieved higher accuracy (≥0.88) in identifying non-infested grains and grains infested by maize weevil, with a correct classification from 0.78 to 1.00 for validation and test sets. Hence, the proposed approach using enhanced radiographs has the potential to provide precise control of Sitophilus zeamais for safe human consumption of maize grains. The proposed method can automatically recognize food contaminated with hidden storage pests without manual features, which makes it more reliable for grain inspection.

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Rapidly Void Detection in TSVs With 2-D X-Ray Imaging and Artificial Neural Networks

IEEE Transactions on Semiconductor Manufacturing ◽

10.1109/tsm.2014.2309591 ◽

2014 ◽

Vol 27 (2) ◽

pp. 246-251 ◽

Cited By ~ 6

Author(s):

Fuliang Wang ◽

Feng Wang

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

X Ray ◽

Void Detection ◽

X Ray Imaging ◽

Artificial Neural

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Transfer Learning to Detect COVID-19 Automatically from X-Ray Images Using Convolutional Neural Networks

International Journal of Biomedical Imaging ◽

10.1155/2021/8828404 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Mundher Mohammed Taresh ◽

Ningbo Zhu ◽

Talal Ahmed Ali Ali ◽

Asaad Shakir Hameed ◽

Modhi Lafta Mutar

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Transfer Learning ◽

Convolutional Neural Networks ◽

Detection Accuracy ◽

X Rays ◽

X Ray ◽

Specificity And Sensitivity ◽

X Ray Imaging ◽

Novel Coronavirus

The novel coronavirus disease 2019 (COVID-19) is a contagious disease that has caused thousands of deaths and infected millions worldwide. Thus, various technologies that allow for the fast detection of COVID-19 infections with high accuracy can offer healthcare professionals much-needed help. This study is aimed at evaluating the effectiveness of the state-of-the-art pretrained Convolutional Neural Networks (CNNs) on the automatic diagnosis of COVID-19 from chest X-rays (CXRs). The dataset used in the experiments consists of 1200 CXR images from individuals with COVID-19, 1345 CXR images from individuals with viral pneumonia, and 1341 CXR images from healthy individuals. In this paper, the effectiveness of artificial intelligence (AI) in the rapid and precise identification of COVID-19 from CXR images has been explored based on different pretrained deep learning algorithms and fine-tuned to maximise detection accuracy to identify the best algorithms. The results showed that deep learning with X-ray imaging is useful in collecting critical biological markers associated with COVID-19 infections. VGG16 and MobileNet obtained the highest accuracy of 98.28%. However, VGG16 outperformed all other models in COVID-19 detection with an accuracy, F1 score, precision, specificity, and sensitivity of 98.72%, 97.59%, 96.43%, 98.70%, and 98.78%, respectively. The outstanding performance of these pretrained models can significantly improve the speed and accuracy of COVID-19 diagnosis. However, a larger dataset of COVID-19 X-ray images is required for a more accurate and reliable identification of COVID-19 infections when using deep transfer learning. This would be extremely beneficial in this pandemic when the disease burden and the need for preventive measures are in conflict with the currently available resources.

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A detection algorithm for faint sources based on 1-d projection for a lobster-eye x-ray imaging system

Space Telescopes and Instrumentation 2020: Ultraviolet to Gamma Ray ◽

10.1117/12.2560604 ◽

2020 ◽

Author(s):

Tatsuya Sawano ◽

Daisuke Yonetoku ◽

Makoto Arimoto ◽

Jin Li ◽

Tatehiro Mihara ◽

...

Keyword(s):

Imaging System ◽

Detection Algorithm ◽

X Ray ◽

X Ray Imaging ◽

Lobster Eye

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Defects of Platelet Function Recognized by X-Ray Imaging and Scanning Electron Microscopy

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100119806 ◽

1985 ◽

Vol 43 ◽

pp. 610-613

Author(s):

M.G. Baldini ◽

S. Morinaga ◽

D. Minasian ◽

R. Feder ◽

D. Sayre ◽

...

Keyword(s):

Electron Microscopy ◽

Scanning Electron Microscopy ◽

Cell Biology ◽

Imaging Technique ◽

Human Platelets ◽

X Rays ◽

X Ray ◽

X Ray Imaging ◽

Complex Phenomena ◽

Scanning Electron

Contact X-ray imaging is presently developing as an important imaging technique in cell biology. Our recent studies on human platelets have demonstrated that the cytoskeleton of these cells contains photondense structures which can preferentially be imaged by soft X-ray imaging. Our present research has dealt with platelet activation, i.e., the complex phenomena which precede platelet appregation and are associated with profound changes in platelet cytoskeleton. Human platelets suspended in plasma were used. Whole cell mounts were fixed and dehydrated, then exposed to a stationary source of soft X-rays as previously described. Developed replicas and respective grids were studied by scanning electron microscopy (SEM).

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Designing high-resolution slow scan CCD-based TEM camera systems

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010012936x ◽

1992 ◽

Vol 50 (2) ◽

pp. 946-947

Author(s):

James F. Mancuso ◽

William B. Maxwell ◽

Russell E. Camp ◽

Mark H. Ellisman

Keyword(s):

Fiber Optics ◽

Ccd Camera ◽

High Energy ◽

Phosphor Layer ◽

X Ray ◽

Light Production ◽

Camera Systems ◽

X Ray Imaging ◽

Electron Image ◽

Scintillating Fiber

The imaging requirements for 1000 line CCD camera systems include resolution, sensitivity, and field of view. In electronic camera systems these characteristics are determined primarily by the performance of the electro-optic interface. This component converts the electron image into a light image which is ultimately received by a camera sensor.Light production in the interface occurs when high energy electrons strike a phosphor or scintillator. Resolution is limited by electron scattering and absorption. For a constant resolution, more energy deposition occurs in denser phosphors (Figure 1). In this respect, high density x-ray phosphors such as Gd2O2S are better than ZnS based cathode ray tube phosphors. Scintillating fiber optics can be used instead of a discrete phosphor layer. The resolution of scintillating fiber optics that are used in x-ray imaging exceed 20 1p/mm and can be made very large. An example of a digital TEM image using a scintillating fiber optic plate is shown in Figure 2.

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Ionic homeostasis and subcellular element compartmentation

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s042482010013434x ◽

1990 ◽

Vol 48 (2) ◽

pp. 150-151

Author(s):

Ann LeFurgey ◽

Peter Ingram ◽

J.J. Blum ◽

M.C. Carney ◽

L.A. Hawkey ◽

...

Keyword(s):

Leishmania Major ◽

Ionic Homeostasis ◽

X Ray ◽

Functional Studies ◽

Cell Compartments ◽

X Ray Imaging ◽

Resolution Electron ◽

Multiple Cell ◽

Role Of Zn

Subcellular compartments commonly identified and analyzed by high resolution electron probe x-ray microanalysis (EPXMA) include mitochondria, cytoplasm and endoplasmic or sarcoplasmic reticulum. These organelles and cell regions are of primary importance in regulation of cell ionic homeostasis. Correlative structural-functional studies, based on the static probe method of EPXMA combined with biochemical and electrophysiological techniques, have focused on the role of these organelles, for example, in maintaining cell calcium homeostasis or in control of excitation-contraction coupling. New methods of real time quantitative x-ray imaging permit simultaneous examination of multiple cell compartments, especially those areas for which both membrane transport properties and element content are less well defined, e.g. nuclei including euchromatin and heterochromatin, lysosomes, mucous granules, storage vacuoles, microvilli. Investigations currently in progress have examined the role of Zn-containing polyphosphate vacuoles in the metabolism of Leishmania major, the distribution of Na, K, S and other elements during anoxia in kidney cell nuclel and lysosomes; the content and distribution of S and Ca in mucous granules of cystic fibrosis (CF) nasal epithelia; the uptake of cationic probes by mltochondria in cultured heart ceils; and the junctional sarcoplasmic retlculum (JSR) in frog skeletal muscle.

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