In this supervised learning concept, labeled datasets are used to train models and then predict the correct outcomes. The CNN takes in the input image, assigns learnable weights and biases to the various aspects or objects in the image which helps it to differentiate between two separate images. The pre-processing required in CNNs is much lower as compared to other classification algorithms.