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Abstract:
Breast cancer detection through ultrasound imaging
presents challenges due to variability in image quality
and interpretation. This study introduces a novel ResNextbased
U-Net architecture for the segmentation of sonomammograms,
aiming to enhance accuracy and reliability. The
proposed model integrates ResNext blocks within the U-Net
framework, leveraging the residual connections to improve
feature extraction and gradient propagation. We evaluated the
model’s performance across five-folds, comparing it with a
baseline U-Net and with ResNet encoders. Our results indicate
that the inclusion of ResNext blocks improves segmentation performance,
particularly in capturing finer details and enhancing
specificity. The enhanced architecture offers a promising tool
for aiding radiologists in the early detection and diagnosis of
breast cancer, providing a reliable and accurate method for
automated sonomammogram segmentation.

Keywords:
Sonomammograms, Lesion segmentation