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A Bayesian network based sequential inference for diagnosis of diseases from retinal images

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Title: A Bayesian network based sequential inference for diagnosis of diseases from retinal images
Authors: Mitra, Suman K.1 suman_mitra@da-iict.org, Lee, Te-Won2 tewon@salk.edu, Goldbaum, Michael3 mgoldbaum@ucsd.edu
Source: Pattern Recognition Letters. Mar2005, Vol. 26 Issue 4, p459-470. 12p.
Subjects: Ophthalmologists, SADT (System analysis), Structured techniques of electronic data processing, System analysis
Abstract: Abstract: We propose a system that learns from the STARE (STructured Analysis of REtina) database and exploits the experience of ophthalmologists to assist in decision-making regarding the presence or absence of retinal diseases. The developed system automatically detects diseases given a description (a set of manifestations) of a retinal image. The manifestations in the retinal image are usually fed sequentially into the system where the manifestation dependences and order must be learned by the system. We apply naive Bayes classifier which is a simple case of Bayesian network to learn the conditional probabilities and to establish an approximate lookup table for sequential manifestation input. The system interacts with the ophthalmologist in determining the sequence of manifestations for inferring the correct disease. The overall performance of the system is found to be satisfactory and useful by ophthalmologists. [Copyright &y& Elsevier]
Copyright of Pattern Recognition Letters is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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PubType: Academic Journal
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  Data: <searchLink fieldCode="AR" term="%22Mitra%2C+Suman+K%2E%22">Mitra, Suman K.</searchLink><relatesTo>1</relatesTo><i> suman_mitra@da-iict.org</i><br /><searchLink fieldCode="AR" term="%22Lee%2C+Te-Won%22">Lee, Te-Won</searchLink><relatesTo>2</relatesTo><i> tewon@salk.edu</i><br /><searchLink fieldCode="AR" term="%22Goldbaum%2C+Michael%22">Goldbaum, Michael</searchLink><relatesTo>3</relatesTo><i> mgoldbaum@ucsd.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Pattern+Recognition+Letters%22">Pattern Recognition Letters</searchLink>. Mar2005, Vol. 26 Issue 4, p459-470. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Ophthalmologists%22">Ophthalmologists</searchLink><br /><searchLink fieldCode="DE" term="%22SADT+%28System+analysis%29%22">SADT (System analysis)</searchLink><br /><searchLink fieldCode="DE" term="%22Structured+techniques+of+electronic+data+processing%22">Structured techniques of electronic data processing</searchLink><br /><searchLink fieldCode="DE" term="%22System+analysis%22">System analysis</searchLink>
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  Data: Abstract: We propose a system that learns from the STARE (STructured Analysis of REtina) database and exploits the experience of ophthalmologists to assist in decision-making regarding the presence or absence of retinal diseases. The developed system automatically detects diseases given a description (a set of manifestations) of a retinal image. The manifestations in the retinal image are usually fed sequentially into the system where the manifestation dependences and order must be learned by the system. We apply naive Bayes classifier which is a simple case of Bayesian network to learn the conditional probabilities and to establish an approximate lookup table for sequential manifestation input. The system interacts with the ophthalmologist in determining the sequence of manifestations for inferring the correct disease. The overall performance of the system is found to be satisfactory and useful by ophthalmologists. [Copyright &y& Elsevier]
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  Data: <i>Copyright of Pattern Recognition Letters is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.patrec.2004.08.010
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      – SubjectFull: Structured techniques of electronic data processing
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              Text: Mar2005
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