Ensemble CNN

Ensemble Convolutional Neural Networks (CNNs) combine multiple CNN architectures to improve the accuracy and robustness of image classification and other tasks. Current research focuses on integrating CNNs with other models, such as transformers, to leverage both local and global image features, and on employing diverse ensemble methods like voting or weighted averaging to optimize performance. This approach has shown significant improvements in various applications, including medical image analysis (e.g., disease detection and tumor segmentation), deepfake detection, and time series outlier detection, demonstrating the power of ensemble learning for complex pattern recognition problems.