Deep Learning and Shape-Driven Combined Approach for Breast Cancer Tumor Segmentation

Mudassar Ali, Haoji Hu, Tufail Muhammad, Muhammad Ahsan Qureshi, Tariq Mahmood

Abstract: Our aim is to enhance the performance of segmenting breast cancer from medical images by overcoming major challenges, including data insufficiency and complexity when training a model. Using the INbreast dataset, it proposes a hybrid method for improving the segmentation accuracy and computational efficiency by combining M3D-Neural Cellular Automata with Shape-Guided Segmentation. This work aims at optimal performance on complex tasks of tumor segmentation by leveraging shape priors and reducing the need for extensive training data. Results confirm that significant improvements in both accuracy and robustness are achieved, which reduces computation time and improves tumor boundary delineation. The hybrid approach provides a time- and cost-effective method for precise detection in breast cancer diagnosis and treatment.

External IDs:dblp:conf/icacs/AliHMQM25