Breast calcification and abnormal tissue formation were identified as major indicators of breast cancer, where early and accurate screening played a crucial role in reducing mortality rates. In this manuscript, Enhanced Self-attention Based Hierarchical Dilated Convolutional Neural Network for Advanced Detection and Precise Diagnosis of Breast Tumors (SBHDCNN-BTDD) is proposed to improve diagnostic accuracy. Initially, input images are gathered from the BUSUC - Breast Ultrasound Dataset and the input images undergo preprocessing using the Distributed Minimum Error Entropy Kalman Filter (DMEEKF), which effectively removes noise and enhances image quality. Tumor regions were then segmented using Accuracy-Enhanced U-Net (Acc-UNet), producing binary masks for precise region-of-interest extraction. The segmented images were subsequently classified using a Self-Attention-Based Hierarchical Dilated Convolutional Neural Network (SBHDCNN) into benign and malignant categories. To address the limitations of fixed parameter selection in conventional deep learning models, the Secretary Bird Optimization Algorithm (SBOA) was employed to optimally tune the SBHDCNN parameters. The segmentation model achieved an F1-score of 99. 30%, while the final classification network attained an accuracy of 99. 61% and an F1-score of 99. 41%. Comparative experimental results demonstrated that the proposed framework outperformed existing approaches in terms of accuracy, robustness, and overall diagnostic performance.
Prabaharan et al. (Fri,) studied this question.