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.

翻译：乳腺癌对全球生命构成严重威胁，每年夺走众多生命。因此，及时检测对于早期干预和提高生存机会至关重要。利用超声图像准确诊断和分类乳腺肿瘤是医学领域持续存在的挑战，需要前沿解决方案以改进治疗策略。本研究提出了多注意力增强的深度学习框架，用于从超声图像中对乳腺癌肿瘤进行分类和分割。针对超声图像肿瘤分割，提出了一种空间通道注意力机制，采用以InceptionResNet为骨干的新型LinkNet深度学习框架。随后，本文提出了一种集成多注意力框架的深度卷积神经网络（DCNNIMAF），用于将分割后的肿瘤分类为良性、恶性或正常。实验结果表明，分割模型取得了98.1%的准确率，损失率仅为0.6%，同时获得了96.9%的交并比（IoU）和97.2%的Dice系数高分。同样，分类模型达到了99.2%的准确率，损失率低至0.31%。此外，该分类框架在F1分数、精确率和召回率上分别取得了99.1%、99.3%和99.1%的优异表现。通过为乳腺癌早期检测和准确分类提供稳健的框架，本研究成果显著推进了医学图像分析领域的发展，有望提升诊断精度并改善患者预后。