Histopathological image classification stands as a cornerstone in the pathological diagnosis workflow, yet it remains challenging due to the inherent complexity of histopathological images. Recently, ...

Auroral image classification has long been a focus of research in auroral physics. However, current methods for automatic auroral classification typically assume that only one type of aurora is ...

These results demonstrate that MABEC-Net, with its multi-scale feature extraction and attention mechanisms, is well-suited for the challenges posed by remote sensing image classification, delivering ...

This project demonstrates a deep learning approach for multi-class image classification using Convolutional Neural Networks (CNNs). The model classifies images into predefined categories such as ...

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 ...

Training a Large CNN for Image Classification: Researchers developed a large CNN to classify 1.2 million high-resolution images from the ImageNet LSVRC-2010 contest, spanning 1,000 categories. The ...

Overview In this project, we develop a CNN architecture tailored specifically for multiclass image classification tasks. The CNN is trained on a diverse dataset comprising images of different ...