Dual-Intended Deep Learning Model for Breast Cancer Diagnosis in Ultrasound Imaging.

ABSTRACT: Automated medical data analysis demonstrated a significant role in modern medicine, and cancer diagnosis/prognosis to achieve highly reliable and generalizable systems. In this study, an automated breast cancer screening method in ultrasound imaging is proposed. A convolutional deep autoencoder model is presented for simultaneous segmentation and radiomic extraction. The model segments the breast lesions while concurrently extracting radiomic features. With our deep model, we perform breast lesion segmentation, which is linked to low-dimensional deep-radiomic extraction (four features). Similarly, we used high dimensional conventional imaging throughputs and applied spectral embedding techniques to reduce its size from 354 to 12 radiomics. A total of 780 ultrasound images-437 benign, 210,

SUBMITTER: Vigil N 

PROVIDER: S-EPMC9179519 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature