Attention Infused U-Net for Robust Breast Tumor Segmentation on the BUSI Dataset

The task of Segmentation and identification of breast tumors are highly significant in the aspect of early breast cancer diagnosis and treatment planning. In spite of the complexity from noise, low contrast, and irregular tumor boundaries, this task is very important in the ultrasound images. One of the greatest tools for these types of challenges and delving into the channels of development for a sound automated analysis is deep learning. This research has discussed mainstream methods like the U-Net and convolutional neural networks (CNNs), as well as hybrid models having attention mechanisms and generative adversarial networks (GANs) applied to the Breast Ultrasound Images (BUSI) dataset to enhance the accuracy, reliability, and also efficiency. The matters reviewed also entail challenges like imbalanced datasets, domain shifts, and noise, among others, and proposed solutions such as data augmentation, domain adaptation, and architectural innovations. The review pattern shifts and delineates the transformative use of deep learning in breast tumor segmentation and integration into practice.