Breast cancer represents the second leading cause of cancer deaths in women today and it
is the most common type of cancer in women. This paper presents some experiments for …

This paper proposes a new classification method based on association rule mining. This
association rule-based classifier is experimented on a real dataset; a database of medical …

This paper presents two classification systems for medical images based on association rule
mining. The system we propose consists of: a pre-processing phase, a phase for mining the …

Using several association and classification approaches to study breast cancer patterns, this
study illustrates how these approaches can be used to predict and diagnose the occurrence …

In this paper, we present a novel method for the classification of mammograms using a
unique weighted association rule based classifier. Images are preprocessed to reveal …

Abstract Breast Cancer Diagnosis and Prognosis are two medical applications pose a great
challenge to the researchers. The use of machine learning and data mining techniques has …

The classification of benign and malignant patterns in digital mammograms is one of most
important and significant processes during the diagnosis of breast cancer as it helps …

In this paper, we propose a method based on association rule-mining to enhance the
diagnosis of medical images (mammograms). It combines low-level features automatically …

This paper presents a study about breast cancer prediction based on data mining methods
to discover an effective way to predict breast cancer. The objective of this paper is to …

Feature extraction is a fundamental step when mammography image analysis is addressed
using learning based approaches. Traditionally, problem dependent handcrafted features …