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Proceedings Paper

Artificial neural network for pulmonary nodule detection: preliminary human observer comparison
Author(s): Seema Garg; Carey E. Floyd; Carl E. Ravin
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Paper Abstract

A single-layer artificial neural network was developed to detect synthetic pulmonary nodules of approximately the same size in patient chest radiographs. The identical detection task was given to human observers with varying degrees of radiological training (board-certified radiologists, residents, and a medical student). The network and human observers were presented five patient radiographs each with 12 marked locations. The human observers estimated the probability that a nodule was present at each of these locations. The network evaluated the same locations for the presence of a nodule. Using Reciever Operating Characteristic (ROC) analysis, we found that the performance of the artificial neural network was comparable to that of human observer. The areas under the curve for the neural network and human observers were 0.93 and 0.92, repectively.

Paper Details

Date Published: 11 May 1994
PDF: 7 pages
Proc. SPIE 2167, Medical Imaging 1994: Image Processing, (11 May 1994); doi: 10.1117/12.175098
Show Author Affiliations
Seema Garg, Duke Univ. Medical Ctr. (United States)
Carey E. Floyd, Duke Univ. Medical Ctr. (United States)
Carl E. Ravin, Duke Univ. Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 2167:
Medical Imaging 1994: Image Processing
Murray H. Loew, Editor(s)

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