Example Concept: CNNs handle image classification by passing input images through a series of layers, including convolutional layers that apply filters to detect features, pooling layers that reduce dimensionality, and fully connected layers that output class probabilities. The convolutional layers use filters to scan the image and create feature maps, which are then downsampled by pooling layers to focus on the most important features. Finally, the fully connected layers interpret these features to classify the image into predefined categories.