Identifying Breast Cancer Using Machine Learning Algorithms

Abstract

Breast cancer is a leading cause of death for women in underdeveloped nations, where early detection and treatment are crucial. This study explores the effectiveness of various machine-learning techniques in breast cancer detection through image processing, including CNNs, transfer learning models (AlexNet, Inception V3), SVMs, and traditional algorithms like Extreme Gradient Boosting and Naive Bayesian classifiers. Optimization techniques such as Particle Swarm Optimization (PSO) are integrated to enhance performance. A comprehensive literature survey highlights existing methodologies and achievements, providing insights for future research in this critical domain.