Explore the first part of our series on sleep stage classification using Python, EEG data, and powerful libraries like Sklearn and MNE. Perfect for data scientists and neuroscience enthusiasts! # ...

Using single-scale feature maps for classification training, the network generates large prediction regions and cannot adequately characterize image details. Therefore, the multi-scale feature fusion ...

Hyperspectral image (HSI) classification involves assigning unique labels to each pixel to identify various land cover categories. While deep classifiers have achieved high predictive accuracy in this ...

Extensive experiments on four medical image datasets show that NGUF is effective in mitigating the model's prediction bias and has superior performance to other state-of-the-art GCD algorithms. Our ...

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 ...

Learn how to train AI models for image recognition and classification. This guide provides an overview of what you need to accomplish image ...

This repository contains code for a binary image classification model to detect pneumothorax using the ResNet-50 V2 architecture. It includes essential steps such as dataset splitting, image ...