Neural network-based colonoscopic diagnosis using on-line learning and differential evolution
Magoulas, George D. and Plagianakos, V.P. and Vrahatis, M.N. (2004) Neural network-based colonoscopic diagnosis using on-line learning and differential evolution. Applied Soft Computing 4 (4), pp. 369-379. ISSN 1568-4946.
In this paper, on-line training of neural networks is investigated in the context of computer-assisted colonoscopic diagnosis. A memory-based adaptation of the learning rate for the on-line back-propagation (BP) is proposed and used to seed an on-line evolution process that applies a differential evolution (DE) strategy to (re-) adapt the neural network to modified environmental conditions. Our approach looks at on-line training from the perspective of tracking the changing location of an approximate solution of a pattern-based, and thus, dynamically changing, error function. The proposed hybrid strategy is compared with other standard training methods that have traditionally been used for training neural networks off-line. Results in interpreting colonoscopy images and frames of video sequences are promising and suggest that networks trained with this strategy detect malignant regions of interest with accuracy.
|Keyword(s) / Subject(s):||minimally invasive imaging procedures, back-propagation networks, medical image interpretation, on-line learning, differential evolution strategies, artificial evolution|
|School:||Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems|
|Research Centre:||Birkbeck Knowledge Lab|
|Depositing User:||Sandra Plummer|
|Date Deposited:||05 Apr 2006|
|Last Modified:||02 Dec 2016 13:23|
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