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

Overview

Journal J Med Syst

Specialty Medical Informatics

Date 2011 Sep 28

PMID 21947904

Citations 7

Authors

J Dheeba

S Tamil Selvi

Affiliations

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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.

Citing Articles

Breast Cancer Segmentation Methods: Current Status and Future Potentials.

Michael E, Ma H, Li H, Kulwa F, Li J Biomed Res Int. 2021; 2021:9962109.

PMID: 34337066 PMC: 8321730. DOI: 10.1155/2021/9962109.

False Positive Reduction by an Annular Model as a Set of Few Features for Microcalcification Detection to Assist Early Diagnosis of Breast Cancer.

Hernandez-Capistran J, Martinez-Carballido J, Rosas-Romero R J Med Syst. 2018; 42(8):134.

PMID: 29915992 DOI: 10.1007/s10916-018-0989-3.

Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection.

Jalalian A, Mashohor S, Mahmud R, Karasfi B, Saripan M, Ramli A EXCLI J. 2017; 16:113-137.

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An Improved CAD System for Breast Cancer Diagnosis Based on Generalized Pseudo-Zernike Moment and Ada-DEWNN Classifier.

Singh S, Urooj S J Med Syst. 2016; 40(4):105.

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