EXTENDED MAMMOGRAM CLASSIFICATION FROM TEXTURAL FEATURES

EXTENDED MAMMOGRAM CLASSIFICATION FROM TEXTURAL FEATURES

The efficient analysis of digital mammograms has an important role in the early detection of breast cancer and can lead to a higher percentage of recovery. This paper presents an extended computer-aided diagnosis system for the classification of mammograms into three classes (normal, benign and mal...

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Bibliographic Details
Main Authors:	Adél BAJCSI, Camelia CHIRA, Anca ANDREICA
Format:	Article
Language:	English
Published:	Babes-Bolyai University, Cluj-Napoca 2023-02-01
Series:	Studia Universitatis Babes-Bolyai: Series Informatica
Subjects:	Breast cancer detection, Mammogram classification, GLRLM, Feature selection, Random Forests, MIAS, DDSM
Online Access:	http://193.231.18.162/index.php/subbinformatica/article/view/5799

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