# Density-Wise Two Stage Mammogram Classification using Texture Exploiting Descriptors

## TL;DR

This paper introduces new texture-based descriptors, DP-HOT and DP-PB-DCT, for mammogram classification that incorporate density information, achieving over 92% accuracy on the IRMA database.

## Contribution

The paper proposes novel texture descriptors combined with feature selection for density-wise mammogram classification, outperforming existing methods.

## Key findings

- Achieved over 92% accuracy on IRMA database mammogram classification.
- Demonstrated the effectiveness of density-aware texture descriptors.
- Compared new descriptors with standard ones, showing superior performance.

## Abstract

Breast cancer is becoming pervasive with each passing day. Hence, its early detection is a big step in saving the life of any patient. Mammography is a common tool in breast cancer diagnosis. The most important step here is classification of mammogram patches as normal-abnormal and benign-malignant. Texture of a breast in a mammogram patch plays a significant role in these classifications. We propose a variation of Histogram of Gradients (HOG) and Gabor filter combination called Histogram of Oriented Texture (HOT) that exploits this fact. We also revisit the Pass Band - Discrete Cosine Transform (PB-DCT) descriptor that captures texture information well. All features of a mammogram patch may not be useful. Hence, we apply a feature selection technique called Discrimination Potentiality (DP). Our resulting descriptors, DP-HOT and DP-PB-DCT, are compared with the standard descriptors. Density of a mammogram patch is important for classification, and has not been studied exhaustively. The Image Retrieval in Medical Application (IRMA) database from RWTH Aachen, Germany is a standard database that provides mammogram patches, and most researchers have tested their frameworks only on a subset of patches from this database. We apply our two new descriptors on all images of the IRMA database for density wise classification, and compare with the standard descriptors. We achieve higher accuracy than all of the existing standard descriptors (more than 92%).