Why do we prefer Convolutional Neural networks (CNN) over Artificial Neural networks (ANN) for image data as input?

Data Science, AI and ML
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#1

1. Feedforward neural networks can learn a single feature representation of the image but in the case of complex images, ANN will fail to give better predictions, this is because it cannot learn pixel dependencies present in the images.

2. CNN can learn multiple layers of feature representations of an image by applying filters, or transformations.

3. In CNN, the number of parameters for the network to learn is significantly lower than the multilayer neural networks since the number of units in the network decreases, therefore reducing the chance of overfitting.

4. Also, CNN considers the context information in the small neighborhood and due to this feature, these are very important to achieve a better prediction in data like images. Since digital images are a bunch of pixels with high values, it makes sense to use CNN to analyze them. CNN decreases their values, which is better for the training phase with less computational power and less information loss.