This paper presents a comparative study between wavelet and curvelet transform for breast
cancer diagnosis in digital mammogram. Using multiresolution analysis, mammogram …

This paper investigates a pattern recognition framework in order to determine and classify
breast cancer cases. Initially, a two-class separation study classifying normal and abnormal …

During the last decade several algorithms have been proposed for automatic mass detection
in mammographic images. However, almost all these methods suffer from a high number of …

The purpose of this study is to implement accurate methods of detection and classification of
benign and malignant breast masses in mammograms. Our new proposed method, which …

We applied an automatic pattern recognition technique, known as Support Vector Machine
(SVM), to classify volcanic tremor data recorded during different states of activity at Etna …

The high number of false positives and the resulting number of avoidable breast biopsies
are the major problems faced by current mammography Computer Aided Detection (CAD) …

The paper offers a crossbreed streamlining algorithm combining harmony search (HS) and
simulated annealing (SA) known as harmony search and simulated annealing (HS-SA) for …

In this work, gray‐scale invariant ranklet texture features are proposed for false positive
reduction (FPR) in computer‐aided detection (CAD) of breast masses. Two main …

Regions of interest (ROIs) found on breast radiographic images are classified as either
tumoral mass or normal tissue by means of a support vector machine classifier …

One of the parameters that are usually stored for mammograms is the BI‐RADS density,
which gives an idea of the breast tissue composition. In this work, we study the effect of BI …