<title>An information gathering system for medical image inspection</title>

The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak. Most governments, enterprises, and scientific research institutions are participating in the COVID-19 struggle to curb the spread of the pandemic. As a powerful tool against COVID-19, artificial intelligence (AI) technologies are widely used in combating this pandemic. In this survey, we investigate the main scope and contributions of AI in combating COVID-19 from the aspects of disease detection and diagnosis, virology and pathogenesis, drug and vaccine development, and epidemic and transmission prediction. In addition, we summarize the available data and resources that can be used for AI-based COVID-19 research. Finally, the main challenges and potential directions of AI in fighting against COVID-19 are discussed. Currently, AI mainly focuses on medical image inspection, genomics, drug development, and transmission prediction, and thus AI still has great potential in this field. This survey presents medical and AI researchers with a comprehensive view of the existing and potential applications of AI technology in combating COVID-19 with the goal of inspiring researchers to continue to maximize the advantages of AI and big data to fight COVID-19.

Download Full-text

Modern TEM systems architecture

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100144486 ◽

1986 ◽

Vol 44 ◽

pp. 602-605

Author(s):

U. Gross ◽

P. Hagemann

Keyword(s):

Electron Microscope ◽

Information Gathering ◽

Systems Architecture ◽

Control Functions ◽

Transmission Electron ◽

New Information ◽

Comprehensive Information ◽

Scanning Transmission ◽

Diffraction Patterns ◽

Analytical Equipment

By addition of analytical equipment, scanning transmission accessories and data processing equipment the basic transmission electron microscope (TEM) has evolved into a comprehensive information gathering system. This extension has led to increased complexity of the instrument as compared with the straightforward imaging microscope, since in general new information capacity has required the addition of new control hardware. The increased operational complexity is reflected in a proliferation of knobs and buttons.In the conventional electron microscope design the operating panel of the instrument has distinct control elements to alter optical conditions of the microscope column in different modes. As a consequence a multiplicity of control functions has been inevitable. Examples of this are the three pairs of focus and magnification controls needed for TEM imaging, diffraction patterns, and STEM images.

Download Full-text

Information gathering behavior on functional foods with a focus on eye tracking of health claims

PsycEXTRA Dataset ◽

10.1037/e514472015-141 ◽

2014 ◽

Author(s):

Masami Asakawa ◽

Masao Okano

Keyword(s):

Eye Tracking ◽

Functional Foods ◽

Information Gathering ◽

Health Claims

Download Full-text

Saccadic guidance for information gathering in natural scenes

10.32470/ccn.2018.1099-0 ◽

2018 ◽

Author(s):

Kirsty McNaught ◽

Maneesh Sahani

Keyword(s):

Information Gathering ◽

Natural Scenes

Download Full-text

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit183863 ◽

2018 ◽

pp. 194-200

Author(s):

J. Magelin Mary ◽

Chitra K. ◽

Y. Arockia Suganthi

Keyword(s):

Genetic Algorithm ◽

Image Processing ◽

Medical Image ◽

Medical Image Processing ◽

Processing Technique ◽

Input Image ◽

Image Processing Technique ◽

Computational Time ◽

Image Mining ◽

The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

Download Full-text