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, ...

This study evaluates the performance and reliability of a vision transformer (ViT) compared to convolutional neural networks (CNNs) using the ResNet50 model in classifying lung cancer from CT images ...

This project demonstrates how to build a Convolutional Neural Network (CNN) using PyTorch to classify images from the Fashion MNIST dataset. The dataset consists of grayscale images of various fashion ...

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 ...

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.