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 …

Runoff prediction plays a crucial role in the scheduling and management of water resources.
A novel enhanced long short-term memory (LSTM) model called LN-LSTM-PSO is proposed …

Industrial defect detection is gaining importance in the control of industrial product quality.
Highly accurate and efficient defect detection with complex and variable industrial defect …

Deep Convolutional Neural Networks (DCNNs) are hard and time-consuming to train.
Normalization is one of the effective solutions. Among previous normalization methods …

This paper proposes a novel framework for generating synthesized PET images from MRIs
to fill in missing PETs and help with Alzheimer's disease (AD) diagnosis. This framework …

At present, the underwater autonomous operation based on monocular vision has poor
accuracy and low intelligence, due mainly to the low accuracy of pose estimation. To solve …

Positron Emission Tomography (PET) has become a preferred imaging modality for cancer
diagnosis, radiotherapy planning, and treatment responses monitoring. Accurate and …

This research aims to develop a novel deep learning network, GBU-Net, utilizing a group-
batch-normalized U-Net framework, specifically designed for the precise semantic …

Left ventricle (LV) segmentation is critical for clinical quantification and diagnosis of cardiac
images. In this work, we propose two novel deep learning architectures called LNU-Net and …

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 …