CBAM-RIUnet: Breast Tumor Segmentation With Enhanced Breast Ultrasound and Test-Time Augmentation.

This study addresses the challenge of precise breast tumor segmentation in ultrasound images, crucial for effective Computer-Aided Diagnosis (CAD) in breast cancer. We introduce CBAM-RIUnet, a deep learning (DL) model for automated breast tumor segmentation in breast ultrasound (BUS) images. The model, featuring an efficient convolutional block attention module residual inception Unet, outperforms existing models, particularly excelling in Dice and IoU scores.

Diagnostic Performance of Deep Learning in Screened Mammogram: Systematic Review.

Background: The usage of artificial intelligence in medical image analysis has significantly surpassed that of earlier related technologies. This paper aimed to investigate the diagnostic accuracy of Artificial Intelligence (AI) based-deep learning models for breast cancer detection.

Method: We used the PICO (Patient/Population/Problem, Intervention, Comparison, Outcome) scheme to formulate the research question and construct our search terms.