Neural attractor network for application in visual field data classification

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

The purpose was to introduce a novel method for computer-based classification of visual field data derived from perimetric examination, that may act as a 'counsellor', providing an independent 'second opinion' to the diagnosing physician. The classification system consists of a Hoptield-type neural attractor network that obtains its input data from perimetric examination results. An iterative relaxation process determines the states of the neurons dynamically. Therefore, even 'noisy' perimetric output, e.g., early stages of a disease, may eventually be classified correctly according to the predefined idealized visual field defect (scotoma) patterns, stored as attractors of the network, that are found with diseases of the eye, optic nerve and the central nervous system. Preliminary tests of the classification system on real visual field data derived from perimetric examinations have shown a classification success of over 80%. Some of the main advantages of the Hopfield-attractor-network-based approach over feed-forward type neural networks are: (1) network architecture is defined by the classification problem; (2) no training is required to determine the neural coupling strengths; (3) assignment of an auto-diagnosis confidence level is possible by means of an overlap parameter and the Hamming distance. In conclusion, the novel method for computer-based classification of visual field data, presented here, furnishes a valuable first overview and an independent 'second opinion' in judging perimetric examination results, pointing towards a final diagnosis by a physician. It should not be considered a substitute for the diagnosing physician. Thanks to the worldwide accessibility of the Internet, the classification system offers a promising perspective towards modern computer-assisted diagnosis in both medicine and tele-medicine, for example and in particular, with respect to non-ophthalmic clinics or in communities where perimetric expertise is not readily available.

Original languageEnglish (US)
Pages (from-to)2799-2809
Number of pages11
JournalPhysics in Medicine and Biology
Volume49
Issue number13
DOIs
StatePublished - Jul 7 2004
Externally publishedYes

Fingerprint

visual fields
Visual Fields
Neural networks
physicians
examination
Physicians
Computer-Assisted Diagnosis
Referral and Consultation
Medicine
telemedicine
Scotoma
Hamming distance
Optic Nerve Diseases
central nervous system
Telemedicine
nerves
Neurology
Relaxation processes
Network architecture
neurons

ASJC Scopus subject areas

  • Biomedical Engineering
  • Physics and Astronomy (miscellaneous)
  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology

Cite this

Neural attractor network for application in visual field data classification. / Fink, Wolfgang.

In: Physics in Medicine and Biology, Vol. 49, No. 13, 07.07.2004, p. 2799-2809.

Research output: Contribution to journalArticle

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