Advanced AI Programming Challenge: Develop a Neural Network for Image Classification

Design and implement a convolutional neural network (CNN) architecture for image classification. Your model should be able to process and analyze input images to accurately predict the correct class labels. Consider factors such as network depth, kernel sizes, pooling layers, and activation functions to optimize the performance of your model.

Instructions:

1. Define the architecture of your CNN, including the number of convolutional and pooling layers, as well as the number of filters and neurons in each layer.
2. Preprocess your training dataset by resizing, normalizing, and augmenting the images to improve model generalization.
3. Split your dataset into training and validation sets to train and evaluate the performance of your model.
4. Train your CNN model using an appropriate optimizer and loss function, monitoring metrics such as accuracy and loss to guide model improvement.
5. Fine-tune the hyperparameters of your model through experimentation to achieve optimal performance on the validation set.
6. Test the final model on a separate test dataset to evaluate its accuracy and generalization capabilities.
7. Provide a detailed analysis of your model's performance, discussing any challenges faced and potential areas for future improvement.