Genetic algorithm for feature selection in mammograms for breast masses classification

ABSTRACT

This paper introduces a Computer-Aided Detection (CAD) system for categorizing breast masses in mammogram images from the DDSM database as Benign, Malignant, or Normal. The CAD process involves Pre-processing, Segmentation, Feature Extraction, Feature Selection, and Classification. Three feature selection methods, namely the Genetic Algorithm (GA), t-test, and Particle Swarm Optimization (PSO) are used. In the classification phase, three machine learning algorithms (kNN, multiSVM, and Naive Bayes) are explored. Evaluation metrics like accuracy, AUC, precision, recall, F1-score, MCC, Dice coefficient, and Jaccard coefficient are used for performance assessment. Training and testing accuracy are assessed for the three classes. The system is evaluated using nine algorithm combinations, producing the following AUC values: GA+kNN (0.93), GA+multiSVM (0.88), GA+NB (0.91), t-test+kNN (0.91), t-test+multiSVM (0.86), t-test+NB (0.89), PSO+kNN (0.89), PSO+multiSVM (0.85), and PSO+NB (0.86). The study shows that the GA and kNN combination outperforms others.

KEYWORDS:

• Mammograms
• breast mass
• feature selection
• Genetic algorithm

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

No funding is used to complete this project.

Notes on contributors

G Vaira Suganthi

Dr. Vaira Suganthi G has 20 years of teaching experience. Her area of interest includes Image Processing and Machine Learning.

J Sutha

Dr. Sutha J has more than 25 years of teaching experience. Her area of interest includes Image Processing and Machine Learning.

M Parvathy

Dr. Parvathy M has more than 20 years of teaching experience. Her area of interest include Image Processing, Data Mining, and Machine Learning.

N Muthamil Selvi

Ms. Muthamil Selvi N has 1 year of teaching experience. Her area of interest is Machine Learning.