# Texture CNN for Histopathological Image Classification

## TL;DR

This paper introduces a compact texture-based CNN architecture for classifying benign and malignant breast cancer tissues in histopathological images, achieving high accuracy despite small, unbalanced datasets.

## Contribution

A novel, parameter-efficient texture CNN model tailored for histopathological image classification, addressing dataset limitations and improving diagnostic accuracy.

## Key findings

- Achieves nearly 90% accuracy on BreakHis dataset.
- Uses fewer parameters than traditional deep models.
- Effective in distinguishing benign and malignant tissues.

## Abstract

Biopsies are the gold standard for breast cancer diagnosis. This task can be improved by the use of Computer Aided Diagnosis (CAD) systems, reducing the time of diagnosis and reducing the inter and intra-observer variability. The advances in computing have brought this type of system closer to reality. However, datasets of Histopathological Images (HI) from biopsies are quite small and unbalanced what makes difficult to use modern machine learning techniques such as deep learning. In this paper we propose a compact architecture based on texture filters that has fewer parameters than traditional deep models but is able to capture the difference between malignant and benign tissues with relative accuracy. The experimental results on the BreakHis dataset have show that the proposed texture CNN achieves almost 90% of accuracy for classifying benign and malignant tissues.