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Early detection of masses in digitized mammograms using texture features and neuro-fuzzy model

A neuro-fuzzy model for fast detection of candidate circumscribed masses in digitized mammograms is presented. The breast tissue is scanned using variable window size, for each sub-image co-occurrence matrices in different orientations (/spl theta/=0/spl deg/, 45/spl deg/, 90/spl deg/ and 135/spl de...

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Main Authors: Youssry, N., Abou-Chadi, F.E.Z., El-Sayad, A.M.
Format: Conference Proceeding
Language:English
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Abou-Chadi, F.E.Z.
El-Sayad, A.M.
description A neuro-fuzzy model for fast detection of candidate circumscribed masses in digitized mammograms is presented. The breast tissue is scanned using variable window size, for each sub-image co-occurrence matrices in different orientations (/spl theta/=0/spl deg/, 45/spl deg/, 90/spl deg/ and 135/spl deg/) are calculated and texture features are estimated for each co-occurrence matrix, then the features are used to train neuro-fuzzy models. The classification results reach 100% for abnormal cases and 80% for normal ones.
doi_str_mv 10.1109/NRSC.2003.1217380
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subjects Breast cancer
Breast tissue
Cancer detection
Feature extraction
Histograms
Image edge detection
Image segmentation
Lesions
Mammography
Neoplasms
title Early detection of masses in digitized mammograms using texture features and neuro-fuzzy model
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