Hybrid Convolutional

Hybrid convolutional neural networks (CNNs) represent a significant advancement in deep learning, aiming to leverage the strengths of CNNs with other architectures like transformers and autoencoders to improve performance across diverse applications. Current research focuses on integrating CNNs with these complementary methods to enhance feature extraction, address limitations like capturing long-range dependencies or handling noisy data, and improve model interpretability and robustness. This approach has yielded substantial improvements in various fields, including medical image analysis (segmentation, diagnosis), remote sensing (object detection, classification), and image retrieval, demonstrating the power of hybrid models for complex tasks.