Ultrasound-Based Breast Cancer Diagnosis Using Scale-Aware Modulation Meet Deep Dilated Convolutional Network With Weighted-Leader Search Optimization

Breast cancer (BC) is another leading cause of death for women, after lung cancer. The number of women who pass away from BC can be decreased with early detection. An automated method is necessary for early cancer identification because traditional BC diagnosis takes a long time. To address this issue, this study proposes a Scale-Aware Modulation Meet Deep Dilated Convolutional Network with Weighted-Leader Search Optimization (SMM-DDCNet-WLSO). To begin with, ultrasound (US) pictures are gathered for analysis from the BUSI and UDIAT datasets. After data collection, Trainable Joint Bilateral Filters (TJBF) remove speckle noise from images while preserving important anatomical borders. Next, the DarkNet-53 Convolutional Neural Network (DarkNet-53-CNN) is used for segmentation. This network efficiently captures hierarchical spatial features to differentiate between benign and malignant regions precisely. After that, the Scale-Aware Modulation Meet Deep Dilated Convolutional Neural Network (SMM-DDCNet) method is used for diagnosis. It uses both multi-scale local and global feature representations to improve diagnosis accuracy. Finally, the weighted-leader search optimization (WLSO) algorithm is used to tune the weight parameter efficiently. The populace is guided toward the best answers by the weighted impact of elite leaders in WLSO's simulation of a dynamic leadership-following approach. This harmony between exploration and exploitation greatly enhances convergence precision and stability. This model is unique in that it attains performance measures (99.70% on BUSI, 99.67% on UDIAT), accuracy (99.59% on BUSI, 99.54% on UDIAT), F1-score (99.61% on BUSI, 99.59% on UDIAT), and specificity (99.56% on BUSI, 99.55% on UDIAT), leading to very accurate BC diagnosis.