How is a 3D CNN similar to a 2D CNN?
A 3d CNN remains regardless of what we say a CNN that is very much similar to 2d CNN. Except that it differs in these following points (non-exhaustive listing): Originally a 2d C o nvolution Layer is an entry per entry multiplication between the input and the different filters, where filters and inputs are 2d matrices. (fig.1)
How are 2D Convolutional neural networks different from 3D?
The 2D convolutional kernels are able to leverage context across the height and width of the slice to make predictions. However, because 2D CNNs take a single slice as input, they inherently fail to leverage context from adjacent slices.
What are the pros and cons of 3D technology?
At that time 3D systems did not deliver, they cost too much or were unreliable. Measurement results from 3D inspection were often inaccurate and not repeatable. All this has changed as 3D technologies have become low-cost, very reliable, repeatable, and easy to implement.
How is inference performed on a 3D scan?
Inference was performed with the 2D CNN by taking each slice of a scan, one at a time. Inference with the 3D CNN was performed by iteratively sampling 3D patches from a scan until all voxels in the scan had predictions associated with them.
How to build a convolutional neural network ( CNN )?
Building a Convolutional Neural Network (CNN) in Keras. Deep Learning is becoming a very popular subset of machine learning due to its high level of performance across many types of data. A great way to use deep learning to classify images is to build a convolutional neural network (CNN).
Can a CNN model be used to predict tortuosity?
It is shown that the CNNs can be used to predict the porosity, permeability, and tortuosity with good accuracy. With the usage of the CNN models, the relation between T and \\ (\\varphi\\) has been obtained and compared with the empirical estimate.