## 4 Discussion  
The rapid evolution of ultrasound technology has positioned it as a key tool for diagnosing breast tumors. However, the diagnostic accuracy of ultrasounds remains disputed due to the overlapping features of benign and malignant lesions in ultrasonographic images, leading to subjective interpretations influenced by the operator's skill. To address this, advanced CAD systems have been developed, offering critical support in interpreting sonographic images of tumors. These CAD systems enhance diagnostic accuracy, especially for less experienced physicians, by analyzing intricate and multifaceted image features, thus mitigating subjectivity and improving differentiation between benign and malignant tumors.  
In our study, we investigate a method for extracting features based on mass irregularity within the frequency domain, enhancing the accuracy of mass classification in breast tumor  
Figure 11: Evaluating the AUC from classification outcomes across various feature sets.  
Figure 10: ROC curve of the CAD system using the proposed features and an area index of 0.9242.  
diagnosis via computer-aided B-mode ultrasound imaging. To assess the effectiveness of these newly developed features, we utilized a dataset of 5252 ultrasound images, which included 2507 cases of malignant tumors and 2745 cases of benign tumors. Furthermore, we explored various feature types, evaluating and comparing their efficacy in tumor classification. Unlike the approach by [PERSON] et al. [23] we utilized morphological features, significantly reducing complexity and extensive computation. Simultaneously, by incorporating texture features, our proposed system maintained high accuracy in diagnosing breast tumors.  
This experiment indicates that the proposed features achieved the best performance, with an AUC of 92.4%, accuracy of 92.91%, sensitivity of 89.94%, and specificity of 91.38%, yielding improved classification outcomes over other spatial domain boundary-focused features. Therefore, this result signifies a high likelihood of accurately identifying malignant tumors. Additionally, high positive predictive value (90.29%) and negative predictive value (91.45%) suggest a notable decrease in the frequency of unnecessary biopsies for benign lesions. This enhancement not only boosts diagnostic confidence but also provides a valuable second opinion, mitigating the risk of misdiagnosis. Consequently, this approach can substantially lower the overall costs associated with breast cancer diagnosis in practical clinical settings.  
Future research endeavors could encompass the integration of deep learning, a rapidly advancing and dynamic field that offers a sophisticated framework for the automated extraction of features via a self-learning network. Since high-quality features are fundamental to enhancing classification accuracy, the application of deep learning represents a potentially transformative approach for deriving robust and discriminative features essential for the automated classification of breast tumors. With the advancements in image processing technology, recent studies also demonstrate the effectiveness of deep learning algorithms in medical image analysis. For instance, the development of the CSwin-PNet, which integrates CNNs with Swin Transformers, has shown significant improvements in breast lesion segmentation in ultrasound images [40]. Another study introduced a magnified adaptive-feature pyramid network designed for the automatic detection of microaneuryss, demonstrating its potential to enhance image-based diagnosis [41]. Additionally, an adaptive multi-scale feature pyramid network, known as AMFP-net, has been proposed for the accurate diagnosis of pneumoconiosis from chest X-ray images [42]. These advancements underscore the potential of deep learning approaches to improve diagnostic accuracy and efficiency in breast cancer detection.