Naeem Osama Bin, Saleem Yasir
Department of Electrical Engineering, University of Engineering and Technology, Lahore-Narowal Campus, Narowal 51600, Pakistan.
Department of Computer Engineering, University of Engineering and Technology, Lahore 39161, Pakistan.
J Imaging. 2024 Oct 16;10(10):256. doi: 10.3390/jimaging10100256.
Breast cancer persists as a critical global health concern, emphasizing the advancement of reliable diagnostic strategies to improve patient survival rates. To address this challenge, a computer-aided diagnostic methodology for breast cancer classification is proposed. An architecture that incorporates a pre-trained EfficientNet-B0 model along with channel and spatial attention mechanisms is employed. The efficiency of leveraging attention mechanisms for breast cancer classification is investigated here. The proposed model demonstrates commendable performance in classification tasks, particularly showing significant improvements upon integrating attention mechanisms. Furthermore, this model demonstrates versatility across various imaging modalities, as demonstrated by its robust performance in classifying breast lesions, not only in mammograms but also in ultrasound images during cross-modality evaluation. It has achieved accuracy of 99.9% for binary classification using the mammogram dataset and 92.3% accuracy on the cross-modality multi-class dataset. The experimental results emphasize the superiority of our proposed method over the current state-of-the-art approaches for breast cancer classification.
乳腺癌仍然是一个关键的全球健康问题，这凸显了推进可靠诊断策略以提高患者生存率的重要性。为应对这一挑战，提出了一种用于乳腺癌分类的计算机辅助诊断方法。采用了一种结合预训练的EfficientNet-B0模型以及通道和空间注意力机制的架构。在此研究了利用注意力机制进行乳腺癌分类的效率。所提出的模型在分类任务中表现出色，特别是在整合注意力机制后有显著改进。此外，该模型在各种成像模态中都表现出通用性，在跨模态评估中，它在乳腺病变分类中表现稳健，不仅在乳房X光片中，在超声图像中也是如此。使用乳房X光片数据集进行二分类时，它的准确率达到了99.9%，在跨模态多类数据集上的准确率为92.3%。实验结果强调了我们所提出的方法相对于当前乳腺癌分类的最先进方法的优越性。
