Segmentation of Breast Ultrasound Lesions Using DenseNet Encoder with Global and Spatial Attention Modules

This paper examines the use of deep learning methods for accurately segmenting breast cancer in ultrasound images, which is an important step in computer-aided cancer diagnosis. To improve border location and region-level accuracy, the proposed framework presents a hybrid attention-based segmentation model, combining the DenseNet121 encoder and three specially designed attention modules: global spatial attention (GSA), temporary spatial attention (TSA) and spatial feature enhancement block (SFEB). Its unique approach is a multi-level attention integration strategy that improves both local features and global awareness, allowing models to understand complex breast injuries more effectively. After training and evaluating the model using the Breast Ultrasound pictures (BUSI) dataset, which comprises 780 grayscale ultrasound pictures with matching binary masks and labels such as benign, malignant, or normal. With a mean Intersection over Union (IoU) of 0.94, overall segmentation accuracy of 0.89, Dice coefficient of 0.94, precision of 0.93, and recall of 0.94, the suggested method outperforms traditional Convolutional Neural Network (CNN) based techniques and shows great promise for supporting radiological diagnoses.