As a new optical machine learning framework, the diffractive deep neural network (D2NN) has attracted much attention due to its advantages such as low power consumption, parallel computing, and fast ...

This project implements a CNN for image classification, designed specifically for the CIFAR-10 dataset. The CIFAR-10 dataset contains 60,000 images across 10 different categories (e.g., airplanes, ...

Convolutional neural networks (CNNs) are highly effective deep learning architectures for remote sensing (RS) image classification. However, the interpretability of CNN architecture remains ...

Automatic labeling of fish species using deep learning across different classification strategies Javier Jareño 1 Guillermo Bárcena-González 1 * Jairo Castro-Gutiérrez 2 Remedios Cabrera-Castro 2 ...

The proposed deep-learning algorithm detects three different diseases from features extracted from Optical Coherence Tomography (OCT) images. The deep-learning algorithm uses CNN to classify OCT ...

18-Layer CNN Architecture: Implements a deep CNN with layers including image normalization, convolution, ReLU, max pooling, fully connected layers, and softmax. CIFAR-10 Dataset: Utilizes the CIFAR-10 ...

Using CNN-FE, TL, and fine-tuning deep learning models as examples, this paper compares and analyzes deep learning algorithms for remote sensed image classification.