To solve the missing of underwater sonar images dataset, a dataset of underwater sonar images dataset is established, including synthetic sonar dataset and real sonar dataset. According to the ...

The AttendSeg deep learning model performs semantic segmentation at an accuracy that is almost on-par with RefineNet while cutting down the number of parameters to 1.19 million.

Before the rise of deep learning, traditional machine learning techniques, such as model-based methods (e.g., active shape and appearance models) and atlas-based methods had been shown to achieve good ...

Keywords: deep learning, U-net, medical image segmentation, pulmonarty embolism, CNN -convolutional neural network. Citation: Zhan S, Yuan Q, Lei X, Huang R, Guo L, Liu K and Chen R (2024) BFNet: a ...

Deep-learning based ensemble segmentation technique has been recently introduced in medical image processing in ratio-based sampling for the arteries and veins in abdominal CT scans (Golla et al., ...

Keywords: deep learning, intracerebral hemorrhage, computed tomography, segmentation, volume measurement. Citation: Peng Q, Chen X, Zhang C, Li W, Liu J, Shi T, Wu Y, Feng H, Nian Y and Hu R (2022) ...

To solve the missing of underwater sonar images dataset, a dataset of underwater sonar images dataset is established, including synthetic sonar dataset and real sonar dataset. According to the ...