A Swarm Optimized Neural Network System for Classification of Microcalcification in Mammograms

Authors:
J. Dheeba;S. Tamil Selvi
Affiliations:
Department of Computer Science and Engineering, Noorul Islam University, Kumaracoil, India;Department of Electronics and Communication Engineering, National Engineering College, Kovilpatti, India
Venue:
Journal of Medical Systems
Year:
2012

Citing 10
Cited 3

Swarm intelligence

Swarm intelligence
Effective shape-based retrieval and classification of mammograms

Proceedings of the 2006 ACM symposium on Applied computing
Computer-Based Identification of Breast Cancer Using Digitized Mammograms

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

Pattern Recognition
Detection of microcalcifications in digital mammograms using combined model-based and statistical textural features

Expert Systems with Applications: An International Journal
On combining morphological component analysis and concentric morphology model for mammographic mass detection

IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
An Improved Medical Decision Support System to Identify the Breast Cancer Using Mammogram

Journal of Medical Systems
A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques

IEEE Transactions on Information Technology in Biomedicine
Breast Cancer Diagnosis: Analyzing Texture of Tissue Surrounding Microcalcifications

IEEE Transactions on Information Technology in Biomedicine
A new evolutionary system for evolving artificial neural networks

IEEE Transactions on Neural Networks

An Improved Decision Support System for Detection of Lesions in Mammograms Using Differential Evolution Optimized Wavelet Neural Network

Journal of Medical Systems
Predicting asthma outcome using partial least square regression and artificial neural networks

Advances in Artificial Intelligence
Weaver Ant Colony Optimization-Based Neural Network Learning for Mammogram Classification

International Journal of Swarm Intelligence Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

Early detection of microcalcification clusters in breast tissue will significantly increase the survival rate of the patients. Radiologists use mammography for breast cancer diagnosis at early stage. It is a very challenging and difficult task for radiologists to correctly classify the abnormal regions in the breast tissue, because mammograms are noisy images. To improve the accuracy rate of detection of breast cancer, a novel intelligent computer aided classifier is used, which detects the presence of microcalcification clusters. In this paper, an innovative approach for detection of microcalcification in digital mammograms using Swarm Optimization Neural Network (SONN) is used. Prior to classification Laws texture features are extracted from the image to capture descriptive texture information. These features are used to extract texture energy measures from the Region of Interest (ROI) containing microcalcification (MC). A feedforward neural network is used for detection of abnormal regions in breast tissue is optimally designed using Particle Swarm Optimization algorithm. The proposed intelligent classifier is evaluated based on the MIAS database where 51 malignant, 63 benign and 208 normal images are utilized. The approach has also been tested on 216 real time clinical images having abnormalities which showed that the results are statistically significant. With the proposed methodology, the area under the ROC curve (A z ) reached 0.9761 for MIAS database and 0.9138 for real clinical images. The classification results prove that the proposed swarm optimally tuned neural network highly contribute to computer-aided diagnosis of breast cancer.