Lightweight convolutional neural networks improved lung cancer classification accuracy in histopathological images while ...

Integrating deep learning in optical microscopy enhances image analysis, overcoming traditional limitations and improving classification and segmentation tasks.

Abstract: Background: Machine learning (ML) privacy problems have prompted the creation of privacy-preserving methods, one of which is Federated Learning (FL), which has emerged as an important ...

Abstract: In the ongoing era of noisy intermediate scaled quantum computers, one of the possible applications to search for an advantage of quantum computing is machine learning. Here we report about ...

This project involves the classification of handwritten digits using three different classifiers: Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), and Decision Trees. The goal is to ...

Hello, in the latest code, there is still an extremely slow increase in average test accuracy at this time the required learning rate is 0.2 but the AUC is increasing rapidly. This problem occurs in ...

Dr. James McCaffrey of Microsoft Research details the "Hello World" of image classification: a convolutional neural network (CNN) applied to the MNIST digits dataset. The "Hello World" of image ...

The "Hello World" of image classification is a convolutional neural network (CNN) applied to the MNIST digits dataset. A good way to see where this article is headed is to take a look at the ...