Automatic Breast Mass Segmentation and Classification Using Subtraction of Temporally Sequential Digital Mammograms.

ABSTRACT:

Objective

Cancer remains a major cause of morbidity and mortality globally, with 1 in 5 of all new cancers arising in the breast. The introduction of mammography for the radiological diagnosis of breast abnormalities, significantly decreased their mortality rates. Accurate detection and classification of breast masses in mammograms is especially challenging for various reasons, including low contrast and the normal variations of breast tissue density. Various Computer-Aided Diagnosis (CAD) systems are being developed to assist radiologists with the accurate classification of breast abnormalities.

Methods

In this study, subtraction of temporally sequential digital mammograms and machine learning are proposed for the automatic segmentation and classification of masses. The per

SUBMITTER: Loizidou K 

PROVIDER: S-EPMC9744267 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature