Logistic Regression based Mass Classification using Feature Extraction

Abstract: Breast cancer holds the second position for cancer deaths in women [12]. There are several Computer Aided Detection and Diagnosis (CAD) systems used today in order to aid radiologists in detecting malignant cancers at the early stage. Such systems along with suitable classifiers yield better prediction of cancerous masses. This paper presents a logistic regression model based mass detection and classification based on selected geometrical features from breast DICOM images with an accuracy of 93%. Previous work of Alima et al, resulted in an accuracy of 91% using ANN[4]. The performance of the feature extraction and classification system is developed using the database collected as a part of the dream challenge[2]. Performance results are given in terms of confusion matrices.

Keywords: Microcalcifications, Compactness, Malignancy, Neoplasy, Craniocaudal, Mediolateral