Segmentación Automatizada de Cáncer de Mama en Imágenes de Ultrasonidos Mediante Redes Neuronales Convolucionales

Erick Fabián Donado Fonseca

doi:10.22490/25394088.10453

Vol. 20 No. 1 (2026)

Published 2026-02-23

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Original article

Automated Breast Cancer Segmentation in Ultrasound Images Using Convolutional Neural Networks

DOI: https://doi.org/10.22490/25394088.10453

Erick Fabián Donado Fonseca Universidad Simón Bolívar

Pre-Print (Spanish)

Abstract
References

This research focuses on the development of an automatic system for tumor detection and segmentation in breast ultrasound images using advanced deep learning techniques. A self-learning program was designed based on convolutional neural networks with a U-Net architecture, capable of training on labeled medical datasets to identify and highlight potential cancerous regions. The methodology included preprocessing ultrasound images by resizing them to a uniform 256x256 pixels, splitting the dataset into training and testing sets (80/20), and normalizing the data. Model training incorporated activation functions, dropout regularization, and batch normalization to enhance performance. The system autonomously improves its accuracy by adapting to new patterns while providing visual feedback through segmentation masks that highlight suspicious areas in ultrasound images. Performance was evaluated by visualizing predictions on images from the training dataset. The results demonstrate the model’s ability to detect and segment tumor regions with high accuracy, reducing false positives and supporting timely diagnosis. This approach highlights the clinical potential of convolutional neural networks to improve diagnostic accuracy, efficiency, and ultimately patient outcomes in breast cancer care.

keywords: Image segmentation, Breast cancer, Ultrasound, Deep learning, U-Net, Convolutional neural networks

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How to Cite

Donado Fonseca, E. F. (2026). Automated Breast Cancer Segmentation in Ultrasound Images Using Convolutional Neural Networks. Publicaciones E Investigación, 20(1). https://doi.org/10.22490/25394088.10453

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