A novel multistage CAD system for breast cancer diagnosis

Kübra Karacan*, Tevfik Uyar, Burcu Tunga, M. Alper Tunga

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)2359-2368
Number of pages10
JournalSignal, Image and Video Processing
Issue number5
Publication statusPublished - Jul 2023

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.


  • Breast cancer diagnosis
  • Computer-aided diagnosis
  • Contrast enhancement
  • HDMR
  • Image clustering
  • Image decomposition


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