Literature DB >> 2211042

Neural networks in radiologic diagnosis. I. Introduction and illustration.

J M Boone1, G W Gross, V Greco-Hunt.   

Abstract

Artificial neural networks (NNs) process information in a manner similar to the way the human brain is thought to process information. Neural networks have potential application in radiology as an artificial intelligence technique that can provide computer-aided diagnostic assistance for the practicing radiologist. The basic characteristics of NNs and the manner in which information propagates through an NN are discussed in nontechnical language, to assist the diagnostic radiologist in understanding the basic principles of neurocomputing. Computer-aided diagnosis selection in pediatric chest radiography using NNs is discussed in a companion article.

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Year:  1990        PMID: 2211042     DOI: 10.1097/00004424-199009000-00012

Source DB:  PubMed          Journal:  Invest Radiol        ISSN: 0020-9996            Impact factor:   6.016


  9 in total

1.  Automated recognition of lateral from PA chest radiographs: saving seconds in a PACS environment.

Authors:  John M Boone; Greg S Hurlock; J Anthony Seibert; Richard L Kennedy
Journal:  J Digit Imaging       Date:  2004-01-30       Impact factor: 4.056

2.  Recognition of chest radiograph orientation for picture archiving and communications systems display using neural networks.

Authors:  J M Boone; S Seshagiri; R M Steiner
Journal:  J Digit Imaging       Date:  1992-08       Impact factor: 4.056

3.  Simulation studies of data classification by artificial neural networks: potential applications in medical imaging and decision making.

Authors:  Y Wu; K Doi; C E Metz; N Asada; M L Giger
Journal:  J Digit Imaging       Date:  1993-05       Impact factor: 4.056

4.  Classification of normal and abnormal lungs with interstitial diseases by rule-based method and artificial neural networks.

Authors:  S Katsuragawa; K Doi; H MacMahon; L Monnier-Cholley; T Ishida; T Kobayashi
Journal:  J Digit Imaging       Date:  1997-08       Impact factor: 4.056

5.  A feed forward neural network for classification of bull's-eye myocardial perfusion images.

Authors:  D Hamilton; P J Riley; U J Miola; A A Amro
Journal:  Eur J Nucl Med       Date:  1995-02

6.  Artificial neural network to predict skeletal metastasis in patients with prostate cancer.

Authors:  Jainn-Shiun Chiu; Yuh-Feng Wang; Yu-Cheih Su; Ling-Huei Wei; Jian-Guo Liao; Yu-Chuan Li
Journal:  J Med Syst       Date:  2009-04       Impact factor: 4.460

7.  Using an artificial neural network to diagnose hepatic masses.

Authors:  P S Maclin; J Dempsey
Journal:  J Med Syst       Date:  1992-10       Impact factor: 4.460

Review 8.  Artificial intelligence in medicine and male infertility.

Authors:  D J Lamb; C S Niederberger
Journal:  World J Urol       Date:  1993       Impact factor: 4.226

9.  Artificial neural networks for the diagnosis of atrial fibrillation.

Authors:  T F Yang; B Devine; P W Macfarlane
Journal:  Med Biol Eng Comput       Date:  1994-11       Impact factor: 2.602

  9 in total

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