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Abstract
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Article Information:
Breast Cancer Diagnosis in Digital Mammogram using Statistical Features and Neural Network
R. Nithya and B. Santhi
Corresponding Author: R. Nithya
Submitted: March 18, 2012
Accepted: April 14, 2012
Published: December 15, 2012 |
Abstract:
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In this study, the mammogram is classified as either normal or cancer pattern. In the last few decades
soft computing improves the accuracy of the breast cancer detection in digital mammograms. The standard
approach for diagnosis of breast cancer is biopsy. But biopsy makes patient discomfort, bleeding and infection.
The CAD (Computer Aided Diagnosis) is developed for the reason of avoid unnecessary biopsy. The statistical
features are extracted from the digital mammograms. These features are fed to neural network classifier to
classify it into two classes namely normal and cancer. This study describes neural network classification
technique. Experiments have been conducted on images of DDSM (Digital Database for Screening
Mammography) database. The performance measures are evaluated by confusion matrix. By increasing the
training samples this study reveals the improved classification accuracy. This CAD system achieved 94%
accuracy, 96% sensitivity and 92% specificity for diagnosis of breast cancer.
Key words: Breast cancer, mammograms, neural network, statistical features, , ,
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Abstract
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Cite this Reference:
R. Nithya and B. Santhi, . Breast Cancer Diagnosis in Digital Mammogram using Statistical Features and Neural Network. Research Journal of Applied Sciences, Engineering and Technology, (24): 5480-5483.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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