WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification

Beijing, Feb. 06, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification ...
EurekAlert!

PolyU develops novel AI graph neural network models to unravel interdisciplinary complexities in image recognition and neuroscience

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models ...
AZoOptics

Deep Learning in Optical Microscopy: Advancements and Applications

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

Object-scene semantics correlation analysis for image emotion classification

Introduction: Image emotion classification (IEC), which predicts human emotional perception from images, is a research highlight for its wide applications. Recently, most existing methods have focused ...
Scientific Research Publishing

A Study on the Application of an AI Image Recognition-Based Early Cervical Cancer Screening System ()

1 Department of Scientific Research, Teaching, and Training, People’s Hospital of Qingbaijiang District, Chengdu, China. 2 Department of Pathology, People’s Hospital of Qingbaijiang District, Chengdu, ...
GitHub

GitHub - Vhakash/Ai-image-classifier

A powerful web-based image classification application built with Streamlit and TensorFlow's MobileNetV2 model. Upload any image and get instant AI-powered predictions with confidence scores!
IEEE

Optimizing ResNet-50 for Multiclass Classification: A Multi-Stage Learning Approach

Abstract: In this study, we present a multistage learning pipeline that utilizes the ResNet-50 architecture as a static feature extractor for multiclass image classification problems. This methodology ...
IEEE

Optimizing Hyperparameters in Meta-Learning for Enhanced Image Classification

Abstract: This paper investigates the significance of hyperparameter optimization in meta-learning for image classification tasks. Despite advancements in deep learning, real-time image classification ...
Frontiers

RCTNet: Residual conv-attention transformer network for corn hyperspectral image classification

Classifying corn varieties presents a significant challenge due to the high-dimensional characteristics of hyperspectral images and the complexity of feature extraction, which hinder progress in ...