A novel multistage CAD system for breast cancer diagnosis
Computer-aided diagnosis (CAD) systems are widely used to diagnose breast cancer using mammography screening. In this research, we proposed a new multistage CAD system based on image decomposition with High-Dimensional Model Representation (HDMR) which is a divide-and-conquer algorithm. We used digi...
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Published in | Signal, image and video processing Vol. 17; no. 5; pp. 2359 - 2368 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
London
Springer London
01.07.2023
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
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Summary: | Computer-aided diagnosis (CAD) systems are widely used to diagnose breast cancer using mammography screening. In this research, we proposed a new multistage CAD system based on image decomposition with High-Dimensional Model Representation (HDMR) which is a divide-and-conquer algorithm. We used digital mammograms from Digital Database for Screening Mammography as dataset. We neglected BIRADS classification and used a brand-new clustering based on HDMR constant and breast size. To find the best performance of HDMR-based CAD system, we compared different pre-processing settings such as contrast enhancement with CLAHE and HDMR, feature extraction with HDMR, feature scaling, dimension reduction with Linear Discriminant Analysis. We used several Machine Learning algorithms and measured the performance of proposed system for normal–benign–malign classification, cancer detection, mass detection and found that the proposed system achieves
66
%
,
71
%
and
87
%
accuracy, respectively. We were able to achieve
92
%
accuracy,
100
%
sensitivity and
91
%
specificity in specific clusters. These results are comparable with deep learning-based methods although we simplified the pipeline and used brand-new HDMR-based processes. |
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ISSN: | 1863-1703 1863-1711 |
DOI: | 10.1007/s11760-022-02453-3 |