An Experimental Evaluation of Deep Learning Networks for Automated Breast Cancer Detection

Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide, where early and accurate diagnosis plays a vital role in improving survival rates. Recent advancements in deep learning have demonstrated significant potential in automating the analysis of medical images for cancer detection. This study presents a comprehensive comparative analysis of convolutional neural network (CNN)–based deep learning models for breast cancer classification using ultra-sound and mammography images. Multiple architectures, including a baseline CNN, AlexNet, DenseNet, ResNet50, ResNet101, VGG16, VGG19, and MobileNetV3, were evaluated to classify breast lesions as benign or malignant. Experimental results reveal notable performance differences across imaging modalities. For ultrasound images, AlexNet achieved the highest accuracy of 89%, while DenseNet and the baseline CNN achieved 88% and 85%, respectively. In contrast, mammography-based classification yielded significantly higher performance, with the baseline CNN outperforming deeper architectures in terms of accuracy and F1-score, achieving 97%. The findings demonstrate that model complexity does not necessarily guarantee superior performance and that properly designed shallow CNNs can effectively outperform deeper networks on high-quality mammographic data. This study highlights the potential of deep learning–based computer-aided diagnosis systems to support radiologists in the early detection of breast cancer.

@article{Chakraborty2026,
  title     = {An Experimental Evaluation of Deep Learning Networks for Automated Breast Cancer Detection},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Partha Chakraborty and Umme Aiman Jannat},
  doi       = {10.14569/IJACSA.2026.0170690},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170690}
}