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

[1] Impact of soft clustering on classification of suspicious areas in Digital Mammograms
McLeod, Peter
Verma, Brijesh
ISSNIP 2008: PROCEEDINGS OF THE 2008 INTERNATIONAL CONFERENCE ON INTELLIGENT SENSORS, SENSOR NETWORKS, AND INFORMATION PROCESSING, 2008, : 109 - 113
[2] MULTI-CLUSTER SUPPORT VECTOR MACHINE CLASSIFIER FOR THE CLASSIFICATION OF SUSPICIOUS AREAS IN DIGITAL MAMMOGRAMS
Leod, Peter M. C.
Verma, Brijesh
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS, 2011, 10 (04) : 481 - 494
[3] A Classifier with Clustered Sub Classes for the Classification of Suspicious Areas in Digital Mammograms
Mc Leod, Peter
Verma, Brijesh
2010 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS IJCNN 2010, 2010,
[4] Neural network applied to cluster anomalies in digital mammograms
Soares, HB
Neto, ADD
Carvalho, MAG
ANALYSIS OF BIOMEDICAL SIGNALS AND IMAGES, PROCEEDINGS, 2002, : 332 - 334
[5] Combining SOM based clustering and MGS for classification of suspicious areas within digital mammograms
Mc Leod, Peter
Verma, Brijesh
Panchal, Rinku
PROCEEDINGS OF THE 2007 INTERNATIONAL CONFERENCE ON INTELLIGENT SENSORS, SENSOR NETWORKS AND INFORMATION PROCESSING, 2007, : 413 - 418
[6] Breast cancer: Classification of suspicious regions in digital mammograms based on capsule network
Soulami, Khaoula Belhaj
Kaabouch, Naima
Saidi, Mohamed Nabil
BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2022, 76
[7] Clustering and least square based neural technique for learning and identification of suspicious areas within digital mammograms
McLeod, Peter
Verma, Brijesh
ICCIMA 2007: INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND MULTIMEDIA APPLICATIONS, VOL I, PROCEEDINGS, 2007, : 190 - 194
[8] Mass Classification in Mammograms Using Neural Network
Azli, Effa Adrina
Huddin, Aqilah Baseri
Ibrahim, Mohd Faisal
Samad, Salina Abdul
PROCEEDINGS OF THE 2017 6TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING AND INFORMATICS (ICEEI'17), 2017,
[9] Three Class Classification of Digital Mammograms using Chebyshev Moments and Convolutional Neural Network
Bhuma, Chandra Mohan
PROCEEDINGS OF THE 2020 5TH INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION AND SECURITY (ICCCS-2020), 2020,
[10] Comparative Evaluation of Two Neural Network Based Techniques for the Classification of Microcalcifications in Digital Mammograms
Brijesh K. Verma
Knowledge and Information Systems, 1999, 1 (1) : 107 - 117

← 1 2 3 4 5 →