Classification of Micro-calcification in Mammograms using Scalable Linear Fisher Discriminant Analysis

Zobia Suhail, Erika R. E. Denton, Reyer Zwiggelaar

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

25 Dyfyniadau(SciVal)
156 Wedi eu Llwytho i Lawr (Pure)

Crynodeb

Breast cancer is one of the major causes of death in women. Computer Aided Diagnosis (CAD) systems are being developed to assist radiologists in early diagnosis. Micro-calcifications can be an early symptom of breast cancer. Besides detection, classification of micro-calcification as benign or malignant is essential in a complete CAD system. We have developed a novel method for the classification of benign and malignant micro-calcification using an improved Fisher Linear Discriminant Analysis (LDA) approach for the linear transformation of segmented micro-calcification data in combination with a Support Vector Machine (SVM) variant to classify between the two classes. The results indicate an average accuracy equal to 96% which is comparable to state-of-the art methods in the literature. Graphical Abstract Classification of Micro-calcification in Mammograms using Scalable Linear Fisher Discriminant Analysis.

Iaith wreiddiolSaesneg
Tudalennau (o-i)1475-1485
Nifer y tudalennau11
CyfnodolynMedical and Biological Engineering and Computing
Cyfrol56
Rhif cyhoeddi8
Dyddiad ar-lein cynnar25 Ion 2018
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 01 Awst 2018

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