Raman spectroscopy in biological applications faces challenges due to complex spectra, characterized by peaks of varying widths and significant biological background noise. Convolutional neural ...

Training a Convolutional Neural Network (CNN) for MNIST digit classification using Swift for TensorFlow is a great way to learn how to apply deep learning techniques in Swift. Swift for TensorFlow ...

The latter network is meant to be the standard Fully Connected Layer that is included as the final stage of a typical Convolutional Neural Network (CNN), after which a Soft Max function does the final ...

CNN transfer learning methodology is employed by using convolutional layers to train a neural network with a training set of data containing OCT images (Wang et al., 2021). This work contributes to ...

The dilated convolution algorithm, which is widely used for image segmentation, is applied in the image classification field in this paper. In many traditional image classification algorithms, ...

In this study, we present a novel recurrence plot (RP)-based time-distributed convolutional neural network and long short-term memory (CNN-LSTM) algorithm for the integrated classification of fNIRS ...

Dr. James McCaffrey of Microsoft Research details the 'Hello World' of image classification: a convolutional neural network (CNN) applied to the MNIST digits dataset.