A novel soft cluster neural network for the classification of suspicious areas in digital mammograms

被引：45

作者：

Verma, Brijesh ^{[1
]}

McLeod, Peter ^{[1
]}

Klevansky, Alan ^{[2
]}

机构：

[1] Cent Queensland Univ, Sch Comp Sci, Rockhampton, Qld 4701, Australia

[2] Gold Coast Hosp, Dept Radiol, Gold Coast, Qld 4215, Australia

来源：

PATTERN RECOGNITION | 2009年 / 42卷 / 09期

关键词：

Pattern classification; Neural networks; Clustering algorithms; COMPUTER-AIDED DIAGNOSIS; BREAST-CANCER; MASSES; MICROCALCIFICATIONS; PERFORMANCE; FEATURES;

D O I：

10.1016/j.patcog.2009.02.009

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a novel soft cluster neural network technique for the classification of suspicious areas in digital mammograms. The technique introduces the concept of soft clusters within a neural network layer and combines them with least squares for optimising neural network weights. The idea of soft clusters is proposed in order to increase the generalisation ability of the neural network by providing a mechanism to More aptly depict the relationship between the input features and the subsequent classification as either a benign OF malignant class. Soft clusters with least squares make the training process faster and avoid iterative processes which have many problems. The proposed neural network technique has been tested on the DDSM benchmark database. The results are analysed and discussed in this paper. (C) 2009 Elsevier Ltd. All rights reserved.

引用

页码：1845 / 1852

页数：8

共 50 条

[21] A Swarm Optimized Neural Network System for Classification of Microcalcification in Mammograms
J. Dheeba
S. Tamil Selvi
Journal of Medical Systems, 2012, 36 : 3051 - 3061
[22] Evolutionary neural networks applied to the classification of microcalcification clusters in digital mammograms
Hernandez-Cisneros, Rolando R.
Terashima-Marin, Hugo
2006 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-6, 2006, : 2444 - 2451
[23] Classification of breast abnormalities in digital mammograms using image and BI-RADS features in conjunction with neural network
Panchal, R
Verma, B
PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), VOLS 1-5, 2005, : 2487 - 2492
[24] A neural network based technique to locate and classify microcalcifications in digital mammograms
Verma, B
IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE, 1998, : 1790 - 1793
[25] Evaluation of a neural network classifier for detection of microcalcifications and opacities in digital mammograms
Diahi, JG
Giron, A
Brahmi, D
Frouge, C
Fertil, B
DIGITAL MAMMOGRAPHY, 1998, 13 : 151 - 156
[26] Subclass based parallel learning neural network for classification of masses in mammograms
V. Sivakrithika
K. Dinakaran
Design Automation for Embedded Systems, 2018, 22 : 65 - 79
[27] Subclass based parallel learning neural network for classification of masses in mammograms
Sivakrithika, V.
Dinakaran, K.
DESIGN AUTOMATION FOR EMBEDDED SYSTEMS, 2018, 22 (1-2) : 65 - 79
[28] Classification of individual and clustered microcalcifications in digital mammograms using evolutionary neural networks
Hernandez-Cisneros, Rolando R.
Terashima-Marin, Hugo
MICAI 2006: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 2006, 4293 : 1200 - +
[29] Classification of Benign and Malignant Breast Mass in Digital Mammograms with Convolutional Neural Networks
Zhao, Xin
Wang, Xianheng
Wang, Hongkai
ISICDM 2018: PROCEEDINGS OF THE 2ND INTERNATIONAL SYMPOSIUM ON IMAGE COMPUTING AND DIGITAL MEDICINE, 2018, : 47 - 50
[30] Soft Clustering and Support Vector Machine based Technique for the Classification of Abnormalities in Digital Mammograms
Mc Leod, Peter
Verma, Brijesh
Park, Minyeop
2009 INTERNATIONAL CONFERENCE ON INTELLIGENT SENSORS, SENSOR NETWORKS AND INFORMATION PROCESSING (ISSNIP 2009), 2009, : 179 - 183

← 1 2 3 4 5 →