Breast Ultrasound Tumor Classification Using a Hybrid Multitask CNN-Transformer Network
This work addresses the problem of improving diagnostic accuracy in breast cancer detection for medical imaging, representing an incremental advancement.
The study tackled breast ultrasound tumor classification by proposing a hybrid multitask CNN-Transformer network, achieving an accuracy of 82.7%, sensitivity of 86.4%, and F1 score of 86.0%.
Capturing global contextual information plays a critical role in breast ultrasound (BUS) image classification. Although convolutional neural networks (CNNs) have demonstrated reliable performance in tumor classification, they have inherent limitations for modeling global and long-range dependencies due to the localized nature of convolution operations. Vision Transformers have an improved capability of capturing global contextual information but may distort the local image patterns due to the tokenization operations. In this study, we proposed a hybrid multitask deep neural network called Hybrid-MT-ESTAN, designed to perform BUS tumor classification and segmentation using a hybrid architecture composed of CNNs and Swin Transformer components. The proposed approach was compared to nine BUS classification methods and evaluated using seven quantitative metrics on a dataset of 3,320 BUS images. The results indicate that Hybrid-MT-ESTAN achieved the highest accuracy, sensitivity, and F1 score of 82.7%, 86.4%, and 86.0%, respectively.