Abstract
Breast cancer remains one of the leading causes of mortality among women worldwide, making early detection crucial for improving survival rates. Deep learning-based approaches have shown remarkable potential in automating tumor detection from mammographic images; however, their effectiveness largely depends on the choice of data augmentation strategies and model architecture. In this study, we systematically investigate the impact of six data augmentation techniques and four object detection models on the task of breast tumor detection, using the publicly available DDSM dataset. Each model was trained on preprocessed mammographic images, with individual augmentation techniques applied separately to assess their isolated effects. The augmentations analyzed include Contrast Limited Adaptive Histogram Equalization, random rotation, random translation, horizontal flipping, Contrast Limited Adaptive Histogram Equalization combined with rotation, and Gaussian noise addition. Experimental results demonstrate that Faster Region-based Convolutional Neural Network consistently outperforms the YOLO variants, achieving the best detection result of 91.35% accuracy when combined with cache optimization. Additionally, we found that Contrast Limited Adaptive Histogram Equalization-based contrast enhancement significantly improves detection performance, whereas geometric transformations such as random rotation and horizontal flipping tend to degrade model accuracy.
Citare
@Inproceedings{Dicu2025AnalyzingTI,
author = {Madalina Dicu and Enol García González and Camelia Chira and José R. Villar},
booktitle = {Hybrid Artificial Intelligence Systems},
title = {Analyzing the Impact of Data Augmentation on Tumor Detection and Classification in Mammograms},
year = {2025}
}