A novel multistage CAD system for breast cancer diagnosis

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

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3 Atıf (Scopus)

Özet

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.

Orijinal dilİngilizce
Sayfa (başlangıç-bitiş)2359-2368
Sayfa sayısı10
DergiSignal, Image and Video Processing
Hacim17
Basın numarası5
DOI'lar
Yayın durumuYayınlandı - Tem 2023

Bibliyografik not

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

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