EurekAlert!

Advanced U-net segmentation model using residual grouped convolution and attention mechanism for brain tumor MRI image segmentation

Through a comprehensive evaluation of model complexity and number of parameters, it was determined that the overall performance of the proposed model is the best when eight group convolutions are used ...
The American Journal of Managed Care

Convolutional Neural Network Improves MS Lesion Segmentation

FLAMeS, a new convolutional neural network, enhances MS lesion segmentation accuracy using only T2-weighted FLAIR images, making it more applicable in clinical settings. The algorithm outperformed ...
ascopubs.org

Performance analysis of artificial intelligence–driven convolutional neural network architectures for liver tumor segmentation.

Comparative efficacy and safety of donafenib versus bevacizumab in combination with immune checkpoint inhibitors and interventional therapy for advanced hepatocellular carcinoma: A retrospective ...
EurekAlert!

A novel deep learning model for medical image segmentation with convolutional neural network and transformer

Accurate segmentation of medical images is essential for clinical decision-making, and deep learning techniques have shown remarkable results in this area. However, existing segmentation models that ...
CoinTelegraph

What are convolutional neural networks?

What are convolutional neural networks in deep learning? Convolutional neural networks are used in computer vision tasks, which employ convolutional layers to extract features from input data.