Moreover, the high-accuracy classification ability of the CNN with uncalibrated, binary microstructure images is powerful, and holds implications towards the potential of generating large ...

We report a semi-supervised Vision Transformer (ViT) framework for automated reflection high energy electron diffraction (RHEED) image classification of ferroelectric nitride (ScAlN) materials grown ...

Welcome to Learn with Jay – your go-to channel for mastering new skills and boosting your knowledge! Whether it’s personal development, professional growth, or practical tips, Jay’s got you ...

Environmental images often contain mixed elements, such as forests bordering water bodies or urban areas with vegetation, which complicates the classification task. In this paper, we propose a novel ...

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 ...

Based on studies using high-medium resolution images, convolutional neural networks (CNNs) and semantic segmentation have shown superiority over classical machine learning (ML), particularly in ...

The project titled "Medical Image Classification for Disease Diagnosis Using Convolutional Neural Networks" aims to develop a robust and accurate machine learning model for the automatic ...

Matsuyama, E. , Watanabe, H. and Takahashi, N. (2024) Performance Comparison of Vision Transformer- and CNN-Based Image Classification Using Cross Entropy: A Preliminary Application to Lung Cancer ...