Abstract: Breast cancer is a common and fatal disease among women worldwide. Accurately and early diagnosing of breast cancer plays a pivotal role in improving the prognosis of patients. Recently, advanced techniques of artificial intelligence and natural image classification have been used for the breast cancer image classification task and have become a hot topic for research in machine learning. This paper proposes a fully automatic computerized method for breast cancer classification using two well-established pretrained CNN models, namely VGG16 and ResNet50. Next, the feature extraction process is used to extract features in a hierarchical manner to train a support vector machine classifier. Evaluating the proposed model shows achieving 92% accuracy. In addition, this paper investigates the effect of different factors, highlights its findings, and provides future directions for the research to develop more advanced models.

Abeer S. Alsheddi. “Hierarchical Pretrained Deep Learning Features for the Breast Cancer Classification”. International Journal of Advanced Computer Science and Applications (IJACSA) 14.2 (2023). http://dx.doi.org/10.14569/IJACSA.2023.0140248

@article{Alsheddi2023,
title = {Hierarchical Pretrained Deep Learning Features for the Breast Cancer Classification},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140248},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140248},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {2},
author = {Abeer S. Alsheddi}
}