English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Mammography is a well established imaging technique for showing tissue abnormalities of breast and has been proven to reduce death rate due to breast cancer in screened populations of women. The proposed method classifies the breast tissues according to severity of abnormality (benign or malign) using combined transforms domain features. In this paper two such domains are explored, Discrete Cosine Transform Discrete Wavelet Transform (DCT-DWT) and Discrete Cosine Transform Stationary Wavelet Transform (DCTSWT). The method is tested on 221 mammogram images from the MIAS database. The combined transform domain features proves to be a promising tool for precise classification with SVM classifier. The DCT-DWT domain yields 96.26% accuracy for discrimination between normal-malign samples comparing to DCT-SWT which gives 93.14%. The novelty of the proposed method is demonstrated by comparing with nearest neighbor classification technique.

CHARACTERIZATION OF BREAST TISSUES IN COMBINED TRANSFORMS DOMAIN USING SUPPORT VECTOR MACHINES