Breast Ultrasound Tumor Classification Using a Hybrid Multitask CNN-Transformer Network

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.PMID:38601088 | PMC:PMC11006090 | DOI:10.1007/978-3-031-43901-8_33
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - Category: Radiology Authors: Source Type: research