  Breast cancer poses a profound threat to lives globally, claiming numerous
lives each year. Therefore, timely detection is crucial for early intervention
and improved chances of survival. Accurately diagnosing and classifying breast
tumors using ultrasound images is a persistent challenge in medicine, demanding
cutting-edge solutions for improved treatment strategies. This research
introduces multiattention-enhanced deep learning (DL) frameworks designed for
the classification and segmentation of breast cancer tumors from ultrasound
images. A spatial channel attention mechanism is proposed for segmenting tumors
from ultrasound images, utilizing a novel LinkNet DL framework with an
InceptionResNet backbone. Following this, the paper proposes a deep
convolutional neural network with an integrated multi-attention framework
(DCNNIMAF) to classify the segmented tumor as benign, malignant, or normal.
From experimental results, it is observed that the segmentation model has
recorded an accuracy of 98.1%, with a minimal loss of 0.6%. It has also
achieved high Intersection over Union (IoU) and Dice Coefficient scores of
96.9% and 97.2%, respectively. Similarly, the classification model has attained
an accuracy of 99.2%, with a low loss of 0.31%. Furthermore, the classification
framework has achieved outstanding F1-Score, precision, and recall values of
99.1%, 99.3%, and 99.1%, respectively. By offering a robust framework for early
detection and accurate classification of breast cancer, this proposed work
significantly advances the field of medical image analysis, potentially
improving diagnostic precision and patient outcomes.
