CNNbased
CNN-based refers to methods and systems that rely on convolutional neural networks as the primary computational model for processing data and making predictions. In a CNN-based approach, input data—commonly images or grid-like signals—is passed through layers that apply learned convolutional filters, followed by nonlinear activations and pooling, to produce progressively abstract feature representations and a final output suitable for classification, regression, or structured prediction.
CNN-based methods are dominant in computer vision and are also used in medical imaging, satellite imagery, video
Training typically requires large labeled datasets and gradient-based optimization. Architectures vary in depth and complexity, with
Historically, CNNs originated in the 1980s and 1990s with early work by Yann LeCun, gaining widespread attention
Advantages include parameter sharing and translation invariance, enabling efficient learning of visual patterns. Limitations include substantial