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Fusion of Orthogonal Moment Features for Mammographic Mass Detection and Diagnosis
Faculty
Computer Science
Year:
2020
Type of Publication:
ZU Hosted
Pages:
Authors:
Khalied Mohamed Hosny
Staff Zu Site
Abstract In Staff Site
Journal:
IEEE Access Ieee
Volume:
Keywords :
Fusion , Orthogonal Moment Features for Mammographic Mass
Abstract:
Masses are mammographic nonpalpable signs of breast cancer. These masses could be detected using screening mammography. This paper proposed a system utilizing orthogonal moment invariants (OMIs) features for mammographic masses detection and diagnosis. In this work, three sets of OMIs features were extracted. These OMIs features are Gaussian-Hermite moments (GHMs), Gegenbauer moments (GeMs), and Legendre moments (LMs). The extracted features are fused and presented to the particle swarm optimization (PSO) algorithm for feature selection. The classification step is achieved using the support vector machine (SVM). The proposed system is evaluated using 400 regions, extracted from the DDSM dataset. The obtained results reveal the promising application of OMIs features for masses detection and identification. It shows that fusing the OMIs features produces an acceptable detection performance where the area under the receiver operating characteristics (ROC) curve is Az = 0.969 ± 0.01 and the best performance of OMIs features is Az = 0.856 ± 0.053 for characterizing the malignancy of masses.
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Khalied Mohamed Hosny, "Fast computation of accurate Zernike moments", Springer, 2008
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Ehab Roshdy Mohamed, "FOUR-PHASE PROTOCOL FOR DETECTION, DELETION, PROTECTION AND RECOVERY FROM AUTORUN VIRUS", ScienceDirect, 2018
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Khalied Mohamed Hosny, "Resilient Color Image Watermarking Using Accurate Quaternion Radial Substituted Chebyshev Moments", ACM, 2019
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Nabil Ali Mohamed Lashen, "Copy-move forgery detection of duplicated objects using accurate PCET moments and morphological operators", THE IMAGING SCIENCE JOURNAL, 2018
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