Abstract
Screening mammograms is a repetitive task that causes fatigue and eye strain since for every thousand cases analyzed by a radiologist, only 3–4 are cancerous and thus an abnormality may be overlooked. Computer-aided detection (CAD) algorithms were developed to assist radiologists in detecting mammographic lesions. In this paper, a computer-aided detection and diagnosis (CADD) system for breast cancer is developed. The framework is based on combining principal component analysis (PCA), independent component analysis (ICA), and a fuzzy classifier to identify and label suspicious regions. This is a novel approach since it uses a fuzzy classifier integrated into the ICA model. Implemented and tested using MIAS database. This algorithm results in the classification of a mammogram as either normal or abnormal. Furthermore, if abnormal, it differentiates it into a benign or a malignant tissue. Results show that this system has 84.03% accuracy in detecting all kinds of abnormalities and 78% diagnosis accuracy.
Highlights
Breast cancer is considered one of the most common and fatal cancers among women in the USA [1]
Results demonstrate that combining independent component analysis (ICA) and principal component analysis (PCA) algorithms improves the total algorithm performance in all testing sets over usage of PCA algorithm only
The best result of applying the proposed computer-aided detection and diagnosis (CADD) algorithm is 84.03%. These results indicate that using PCA algorithm for dimensionality reduction before ICA algorithm improves the ICA algorithm accuracy with an average of 50.51%
Summary
Breast cancer is considered one of the most common and fatal cancers among women in the USA [1]. A result of FP is defined to be when a radiologist reports a suspicious change in the breast but no cancer is found after further examinations. It leads to unnecessary biopsies and anxiety. CAD algorithms have been developed to assist radiologists in detecting mammographic lesions. These systems are regarded as a second reader, and the final decision is left to the radiologist. CAD algorithms have improved total radiologist accuracy of detection of cancerous tissues [4]. Swiniarski and Lim [13] integrated ICA with rough set model for breast-cancer detection.
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