Convolutional Neural Network Model

Convolutional neural networks (CNNs) are a class of deep learning models designed for processing grid-like data, primarily images, achieving high accuracy in various classification and detection tasks. Current research emphasizes improving CNN efficiency and robustness through architectural innovations (e.g., ResNet, Inception, efficientNet), ensemble methods, and novel activation functions, as well as addressing challenges like over-smoothing and memory limitations in training. The widespread application of CNNs spans diverse fields, including medical image analysis (disease detection, diagnosis), geophysics (seismic data processing), and even legal applications (document classification), demonstrating their significant impact on both scientific understanding and practical problem-solving.