U-net is an image segmentation technique developed primarily for image segmentation
tasks. These traits provide U-net with a high utility within the medical imaging community …

This article proposes a hybrid multi-dimensional features fusion structure of spatial and
temporal segmentation model for automated thermography defects detection. In addition, the …

The segmentation of blood vessels from retinal images is an important and challenging task
in medical analysis and diagnosis. This paper proposes a new architecture of the U-Net …

Background and objective: The vessel segmentation in CT angiography (CTA) provides an
important basis for automatic diagnosis and hemodynamics analysis. Virtual unenhanced …

Fundus blood vessel image segmentation plays an important role in the diagnosis and
treatment of diseases and is the basis of computer-aided diagnosis. Feature information …

Image segmentation means subdividing the image into different objects. We use different
methods for the segmentation of images. For getting different objects from a single image …

Objective We aimed to develop a deep learning (DL)–based algorithm for early glaucoma
detection based on color fundus photographs that provides information on defects in the …

In this paper, we propose a new convolutional layer called Depthwise-STFT Separable layer
that can serve as an alternative to the standard depthwise separable convolutional layer …

U-net is an image segmentation technique developed primarily for medical image analysis
that can precisely segment images using a scarce amount of training data. These traits …

On CT images, irreversible damage caused by bronchial tuberculosis can be seen, and
early observation and intervention can provide assistance for the patient's prognosis. So in …