Combination of Convolutional Neural Network and AdaBoost for Breast Cancer Diagnosis

Abstract

Breast cancer is a cancer that develops from breast tissue. Early symptoms of breast cancer include the existence of a lump in the breast, a change in breast shape, or dimpling on the skin. This research explores the potential of ensemble learning, with a focus on the AdaBoost algorithm, to enhance the performance of Convolutional Neural Networks (CNN) in image classification tasks, particularly with breast cancer image datasets. The architectures in focus were VGG-16, ResNet50, and Inception V4, three prevalent CNN models with proven efficiency in image recognition tasks. Coupling these CNN models with AdaBoost led to notable performance improvements in individual tests. The study further pushed the envelope by constructing an ensemble model that combined all three CNN models. This ensemble, with AdaBoost, demonstrated impressive performance across various datasets. With precision and recall scores exceeding 0.94, an F1-Score of 0.96, and an overall accuracy of 0.95 to 0.99. The significant performance boost can be attributed to the richer feature space generated by the ensemble of multiple CNN models and the iterative refinement of predictions provided by the AdaBoost algorithm. Despite the ensemble model's complexity and increased computational demand, the results provide a compelling justification for its use. Further research could delve into optimizing such ensemble models, exploring other ensemble strategies, or testing the models on diverse datasets and tasks beyond image classification.