CNN is a successful image classification that uses hierarchical feature extraction, ViTs capture the global context but require substantial data and computation. In this research, we have used ...

The work in this project helps in improving the classification of skin diseases using the combination of Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs). GANs were used ...

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, ...

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 ...

Document image classification has a significant difficulty for the retrieval of digital documents and systems management in recent years. The main goal of this study is to investigate the ...

This project implements a Convolutional Neural Network (CNN) to perform real-time image classification. Initially trained on the MNIST dataset for digit recognition, the application demonstrates core ...

In the field of disease diagnosis where only a small dataset of medical images may be accessible, the light-weight convolutional neural network (CNN) has become popular because it can help to avoid ...