Abstract
Today, mammography is the best method for early detection of breast cancer. Radiologists failed to detect evident cancerous signs in approximately 20% of false negative mammograms. False negatives have been identified as the inability of the radiologist to detect the abnormalities due to several reasons such as poor image quality, image noise, or eye fatigue. This paper presents a framework for a computer aided detection system that integrates Principal Component Analysis (PCA), Fisher Linear Discriminant (FLD), and Nearest Neighbor Classifier (KNN) algorithms for the detection of abnormalities in mammograms. Using normal and abnormal mammograms from the MIAS database, the integrated algorithm achieved 93.06% classification accuracy. Also in this paper, we present an analysis of the integrated algorithm’s parameters and suggest selection criteria.
Highlights
Breast cancer is the most common form of cancer in women; its early detection has proven to save lives
This paper presents a framework for a computer aided detection system that integrates Principal Component Analysis (PCA), Fisher Linear Discriminant (FLD), and Nearest Neighbor Classifier (KNN) algorithms for the detection of abnormalities in mammograms
Our framework for the proposed Computer Aided Detection (CAD) system is based on integrating PCA, FLD, and KNN classifier
Summary
Breast cancer is the most common form of cancer in women; its early detection has proven to save lives. The best known possible remedy and successful treatment for breast cancer is the early detection as it has considerably reduced the mortality rates in the past years [2]. It is very important for women to monitor their risk factors and maintain their periodical screening. The sensitivity of mammography has been reported to improve if two radiologists examine the mammogram [4] This is a costly solution and other alternatives to this problem have to be investigated. The proposed CAD system integrates PCA as a decorrelation-based module, FLD as a dimensionality reduction and feature extraction module, and KNN as a classification module.
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