Fuzzy Analysis of X-ray images for automated disease examination

Craig Watman; Kim Le

Fuzzy Analysis of X-ray images for automated disease examination

Craig Watman, Kim Le

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

Abstract

This paper presents the design of a fuzzy decision system for Cancer and Tuberculosis detection based on X-ray lung images. The system is in a tuning stage based on advices from medical experts. With a training set of 40 positive and 10 negative images, the system can classify correctly 42% positive cases with no false negative results. This is a promising result; however the system needs further tuning with additional features and concise examination rules

Original language	English
Title of host publication	International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2004
Editors	Mircea Gh Negoita, Robert J Howlett, Lakhmi C Jain
Place of Publication	Germany
Publisher	Springer
Pages	491-497
Number of pages	7
ISBN (Print)	9783540232063
Publication status	Published - 2004
Event	Knowledge-Based Intelligent Information and Engineering Systems - Wellington, New Zealand Duration: 22 Sept 2004 → 24 Sept 2004

Conference

Conference	Knowledge-Based Intelligent Information and Engineering Systems
Country/Territory	New Zealand
City	Wellington
Period	22/09/04 → 24/09/04

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

@inproceedings{c21be98ec5e543ff8f6757fa02d241e7,

title = "Fuzzy Analysis of X-ray images for automated disease examination",

abstract = "This paper presents the design of a fuzzy decision system for Cancer and Tuberculosis detection based on X-ray lung images. The system is in a tuning stage based on advices from medical experts. With a training set of 40 positive and 10 negative images, the system can classify correctly 42% positive cases with no false negative results. This is a promising result; however the system needs further tuning with additional features and concise examination rules",

author = "Craig Watman and Kim Le",

year = "2004",

language = "English",

isbn = "9783540232063",

pages = "491--497",

editor = "Negoita, {Mircea Gh} and Howlett, {Robert J} and Jain, {Lakhmi C}",

booktitle = "International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2004",

publisher = "Springer",

address = "Netherlands",

note = "Knowledge-Based Intelligent Information and Engineering Systems ; Conference date: 22-09-2004 Through 24-09-2004",

}

Watman, C & Le, K 2004, Fuzzy Analysis of X-ray images for automated disease examination. in MG Negoita, RJ Howlett & LC Jain (eds), International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2004. Springer, Germany, pp. 491-497, Knowledge-Based Intelligent Information and Engineering Systems, Wellington, New Zealand, 22/09/04.

Fuzzy Analysis of X-ray images for automated disease examination. / Watman, Craig; Le, Kim.
International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2004. ed. / Mircea Gh Negoita; Robert J Howlett; Lakhmi C Jain. Germany: Springer, 2004. p. 491-497.

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

TY - GEN

T1 - Fuzzy Analysis of X-ray images for automated disease examination

AU - Watman, Craig

AU - Le, Kim

PY - 2004

Y1 - 2004

N2 - This paper presents the design of a fuzzy decision system for Cancer and Tuberculosis detection based on X-ray lung images. The system is in a tuning stage based on advices from medical experts. With a training set of 40 positive and 10 negative images, the system can classify correctly 42% positive cases with no false negative results. This is a promising result; however the system needs further tuning with additional features and concise examination rules

AB - This paper presents the design of a fuzzy decision system for Cancer and Tuberculosis detection based on X-ray lung images. The system is in a tuning stage based on advices from medical experts. With a training set of 40 positive and 10 negative images, the system can classify correctly 42% positive cases with no false negative results. This is a promising result; however the system needs further tuning with additional features and concise examination rules

M3 - Conference contribution

SN - 9783540232063

SP - 491

EP - 497

BT - International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2004

A2 - Negoita, Mircea Gh

A2 - Howlett, Robert J

A2 - Jain, Lakhmi C

PB - Springer

CY - Germany

T2 - Knowledge-Based Intelligent Information and Engineering Systems

Y2 - 22 September 2004 through 24 September 2004

ER -

Fuzzy Analysis of X-ray images for automated disease examination

Abstract

Conference

UN SDGs

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