DenseUNet for Breast Cancer Segmentation in Histopathological Images

Abstract

Background: Breast cancer remains one of the leading causes of mortality among women worldwide, highlighting the urgent need for accurate and efficient diagnostic tools. Histopathological image analysis plays a critical role in diagnosis by enabling cellularlevel tissue examination. However, manual assessment is often timeconsuming, subjective, and prone to variability, driving the need for automated solutions. Methods: This study proposes DenseUNet169, a deep learning framework that integrates established DenseNet and UNet for systematic evaluation in breast cancer segmentation. The model improves gradient flow, facilitates efficient training, and enhances segmentation accuracy by focusing on visually salient regions within histopathological images. Performance is evaluated using key metrics, including Intersection over Union, Dice Coefficient, Pixel Accuracy, Modified Hausdorff Distance, Surface Dice Overlap, Log Loss, and Jaccard Index. Results: Experiments conducted on a publicly available breast cancer segmentation dataset demonstrate competitive performance compared to alternative backbone configurations. Conclusion: Given the limited dataset size, the results should be interpreted as a proof of concept evaluation of the proposed architectural integration rather than evidence of largescale clinical deployment readiness.