Here is the synopsis of our sample research paper on Neural Networking in Medical Imaging. Have the paper e-mailed to you 24/7/365.
Essay / Research Paper Abstract
A 5 page assertion that networking is opening vast avenues for increased efficiency and accuracy in the analysis of medical imaging. Traditional methods of data analysis in such applications and medicine relied solely on the visual inspection of that data by experts. Neural networking complements this inspection with detailed computer analysis utilizing complex computer algorithms. The technique has proven invaluable in a number of diverse medical arenas. Bibliography lists 12 sources.
Page Count:
5 pages (~225 words per page)
File: AM2_PPneurNt.rtf
Buy This Term Paper »
Unformatted sample text from the term paper:
for increased efficiency and accuracy in the analysis of medical imaging. Neural networking is just one, however, of the many leaps forward which have been made in recent years
in medicine. Indeed, medicine in the last century has progressed in leaps and bounds. Without corresponding developments in other areas of medicine, neural networking would be impossible.
One of the most progressive areas of medicine, as it relates to neural networking, has been radiology. The technique is useful as well, however, in interpreting endoscopic data such
as that collected in angiograms and colonoscopies. Technological improvements have occurred in practically every aspect of endoscopic testing and radiology, both
in diagnostic techniques and in treatment techniques. Digital imaging in particular is an important advancement which will propel the techniques well into the future. With the coupling of
neural networking and digital imaging comes many new possibilities in terms of data analysis, interpretation, and diagnosis. Medicine, of course, is
only one discipline in which neural networks are quickly making their forefront. The technique relies, in part, on optical correlators which convert optical processing systems into processing products (Bains,
1998). Young and Francis (1998, PG) define neural networks as: "very, very complex computer
algorithms that can perform tasks normally handled by humans, like picking socks or assorting through credit card records looking for fraud".
One of the most widely hailed uses of neural networks is in the computerized scanning of fingerprints. Traditional methods of data analysis
...