# Breast Cancer Classification Using Feature Selection via Improved Simulated Annealing and SVM Classifier

## TL;DR

This paper presents a breast cancer detection system using an improved simulated annealing algorithm and SVM, achieving high accuracy on standard datasets.

## Contribution

The novel contribution is the use of an improved simulated annealing algorithm for feature selection in breast cancer classification.

## Key findings

- The system achieved 99.67% accuracy on the CBIS-DDSM dataset and 98% on the MIAS dataset.
- The ISA algorithm outperformed traditional SA and full-feature approaches in classification performance.

## Abstract

Background: Breast cancer is among the most common cancers in women, and early diagnosis is critical for better treatment outcomes and reduced mortality. Efficient computer-aided diagnostic (CAD) systems play a crucial role in enhancing diagnostic accuracy and facilitating timely clinical decisions. Methods: This study proposes an automated CAD system for detecting cancerous tumors in mammograms, consisting of four stages: preprocessing, feature extraction, feature selection, and classification. In preprocessing, the region of interest (ROI) is extracted, followed by noise suppression and contrast enhancement to improve image quality. Shape, histogram, and tissue-related features are then computed from each ROI. An Improved Simulated Annealing (ISA) algorithm is employed to adaptively select the most informative features through a flexible process and composite fitness function, effectively reducing dimensionality while preserving high classification accuracy. Finally, classification is performed using a Support Vector Machine (SVM) to distinguish between malignant and benign masses. Results: Evaluation on the CBIS-DDSM and MIAS datasets showed the system achieved accuracies of 99.67% and 98%, sensitivities of 99.33% and 98%, and F1-scores of 99.66% and 97.9%, respectively. These results indicate notable improvements over traditional SA and full-feature approaches. Conclusions: The findings confirm the effectiveness of the ISA algorithm in selecting relevant features, thereby enhancing the performance of breast cancer detection.