Convolutional Neural Networks (CNNs) are specifically designed to process grid-like data such as images by using convolutional layers to automatically learn spatial hierarchies of features (edges, textures, shapes).

Step-by-step Derivation:
Step 1: Analyze the task requirement. Image classification requires the model to recognize patterns regardless of their position in the image (translation invariance) and to capture local spatial correlations.
Step 2: Evaluate Option B (LSTM). LSTMs are a specialized type of RNN designed for sequential data (time series, text) and are not efficient for high-dimensional spatial data like images.
Step 3: Evaluate Option C (FNN). While a Feedforward Neural Network can be used, it requires flattening the image into a 1D vector, which destroys spatial structure and leads to an explosion of parameters (overfitting).
Step 4: Evaluate Option A (CNN). CNNs use filters (kernels) that slide across the image to extract local features and pooling layers to reduce dimensionality, making them the industry standard for image classification.