... and subsequently calculate the tumor’s volume. The study addresses challenges related to deep neural networks, such as the requirement for large and diverse datasets, hyperparameter optimization, and potential data bias. To evaluate performance, two convolutional neural network (CNN) architectures, Inception-v3...

... modeling knee deep learning CNN U-Net SegResNet MONAI framework computer-aided design machine learning for engineering applications Accurate segmentation of organs and structures from volumetric images, such as computed tomography (CT) and magnetic resonance (MR) images, is crucial...

... a combination of multi-scale convolutional neural network (MS-CNN) and long short-term memory (LSTM) is proposed. The proposed hybrid multi-scale convolutional LSTM (HMCL) model is capable of extracting both spatial features of various scales and temporal features from the input data to provide accurate RUL...

...Tianyuan Liu; Jinsong Bao; Junliang Wang; Yiming Zhang Machine vision has a wide range of applications in the field of welding. The rise of convolutional neural network (CNN) provides a new way to extract visual features of welding. Due to the limitation of the small size of our molten pool dataset...

... learning-based approach for predicting the stress fields in 2D linear elastic cantilevered structures subjected to external static loads at its free end using convolutional neural networks (CNNs). Two different architectures are implemented that take as input the structure geometry, external loads...