Convolutional Neural Network
Convolutional Neural Networks (CNNs) are a class of deep learning models designed for processing grid-like data, excelling in image analysis and related tasks. Current research focuses on improving CNN efficiency and robustness, exploring architectures like EfficientNet and Swin Transformers, as well as novel approaches such as Mamba models to address limitations in computational cost and long-range dependency capture. This active field of research has significant implications across diverse applications, including medical image analysis (e.g., cancer detection, Alzheimer's diagnosis), damage assessment, and art forgery detection, demonstrating the power of CNNs for automating complex visual tasks.
2331papers
Papers - Page 7
January 16, 2025
Detection of Vascular Leukoencephalopathy in CT Images
Dynamic Neural Style Transfer for Artistic Image Generation using VGG19
Identification of Traditional Medicinal Plant Leaves Using an effective Deep Learning model and Self-Curated Dataset
Mono-Forward: Backpropagation-Free Algorithm for Efficient Neural Network Training Harnessing Local Errors
January 15, 2025
January 14, 2025
Benchmarking Classical, Deep, and Generative Models for Human Activity Recognition
RWKV-UNet: Improving UNet with Long-Range Cooperation for Effective Medical Image Segmentation
Spatio-Temporal Graph Convolutional Networks: Optimised Temporal Architecture
A Feature-Level Ensemble Model for COVID-19 Identification in CXR Images using Choquet Integral and Differential Evolution Optimization
Early prediction of the transferability of bovine embryos from videomicroscopy
January 13, 2025
January 12, 2025
January 11, 2025