• Convolutional Layers: These layers apply convolutional filters to the input images, allowing the model to learn spatial hierarchies of features. Each convolutional layer is followed by a non-linear activation function, typically ReLU (Rectified Linear Unit), to introduce non-linearity into the model. • Pooling Layers: Pooling layers, such as max pooling, are used to down-sample the feature maps, reducing their spatial dimensions while retaining the most salient features. This helps to minimize computational complexity and prevent overfitting. • Fully Connected Layers: After several convolutional and pooling layers, the output is flattened and passed through fully connected layers, which further refine the feature representation. The final output of the CNN is a high-dimensional feature vector that encapsulates the essential characteristics of the input image.