CNN (Convolutional Neural Network)

A CNN (Convolutional Neural Network) is a type of deep learning model specialized for processing visual information such as image recognition and video analysis. Inspired by the structure of the human visual cortex, it excels at hierarchically extracting and recognizing local features (edges, textures, shapes, etc.) from images. The main components of a CNN are: • Convolutional Layer: Generates feature maps by passing filters (kernels) over the image to extract local patterns • Activation Function (e.g., ReLU): Introduces non-linearity to improve expressive power • Pooling Layer: Reduces spatial dimensions, summarizing features and reducing computational load • Fully Connected Layer: Uses the extracted features to perform final classification or regression This architecture enables CNNs to automatically extract features from images — something that was difficult with traditional machine learning algorithms — and they are applied across a wide range of fields including healthcare, manufacturing, retail, and security. Representative applications: • Face recognition and person detection • Object detection and image classification (achieving high accuracy on benchmarks like ImageNet) • Road and traffic sign recognition for autonomous driving • Diagnostic support in medical imaging (e.g., tumor detection) • Automated defect detection and visual inspection CNNs have produced a diverse family of models — AlexNet, VGG, ResNet, EfficientNet, and others — each selected based on the task and available computational resources. They remain the foundational technology for visual AI and continue to evolve.