## 1 Introduction
Additionally, a feature-discriminator is incorporated into the energy function of the level-set method to refine the low confidence pixels at the boundaries. Finally, the outputs of the level set method at different stages are combined to create final feature maps, which further enhance the segmentation process. The proposed approach's performance was evaluated using two datasets, consisting of 349 breast ultrasound images from various hospitals. [PERSON] et al. [17] developed a novel breast cancer diagnosis model called Optimal Multi-Level Thresholding-based Segmentation with DL-enabled Capsule Network (OMLTS-DLCN). The model uses digital mammograms and includes a pre-processing step called Adaptive Fuzzy based median filtering (AFF) to eliminate noise from the mammogram images. The breast cancer segmentation is performed using the [PERSON]'s based Multilevel Thresholding with Shell Game Optimization (OKMT-SGO) algorithm. The model also involves a CapsNet-based feature extractor and a Back-Propagation Neural Network (BPNN) classification model to detect the presence of breast cancer. [PERSON] et al. [18] introduced a model-based deep convolutional neural network feature for the segmentation of the Region of Interest from the pectoral muscle. In the first step, a pre-processing stage is employed, where morphological operations with Otsu's thresholding are utilized to eliminate artifacts and labels in images. Next, Wavelet Transform is used to remove noise, and histogram equalization is applied as the final step of pre-processing to improve image contrast. Artificial Neural Network and Support Vector Machine classifiers are then trained based on deep convolutional neural network features to estimate the Region of Interest from the pectoral muscle. To enhance the segmentation process of mammograms and decrease the number of suspicious regions, a hybrid thresholding method was proposed by [PERSON] et al. [19] for use in CAD systems. This method facilitates fully-automatic segmentation of suspicious regions by utilizing three techniques in tandem: Otsu multilevel thresholding, Havrda & Charvat entropy, and the w-BSAFCM algorithm developed by the authors for image clustering. Toprevent any loss of information, the mammogram is segmented into two sub-images, the pectoral muscle and the breast region, and segmentation is performed on each sub-image. Breast tumor segmentation in mammogram images poses significant challenges due to several factors. The appearance of breast tumors can vary widely, depending on factors such as tumor size, shape, and location. Furthermore, mammogram images can be noisy, and tumors may be challenging to differentiate from the surrounding breast tissue. Overlapping structures, including glandular tissue and blood vessels, can also obscure the tumor boundary. Additionally, limited annotated datasets hinder the training of machine learning and deep learning models for accurate tumor segmentation. Finally, tumor segmentation accuracy can depend on the interpretation skills and experience of the radiologist, making the process subjective. Addressing these challenges is critical for developing effective breast tumor segmentation methods and improving breast cancer diagnosis and treatment outcomes [7; 20]. Our study aims to address the challenges associated with breast tumor segmentation in mammogram images by utilizing a combination of attention mechanism technique and a U-Net model. The proposed methodology is designed to improve the accuracy and efficiency of the segmentation process. Section 2 of the study provides a detailed description of the methodology employed in our model. We explain the design and implementation of the attention mechanism technique and U-Net model and highlight their advantages over traditional segmentation techniques. In Section 3, we present the results of the experiments conducted to evaluate the performance of our model. The results are compared with recently published pipelines to demonstrate the effectiveness of our proposed methodology.