The correct answer is D. Convolutional Neural Network (CNN).
A convolutional neural network (CNN) is a type of deep learning model that is commonly used for image recognition and classification. CNNs are made up of layers of artificial neurons, and each layer is responsible for detecting different features in the image. The first layer might detect edges, the second layer might detect shapes, and so on. The final layer of the CNN will output a probability distribution over the possible classes of the image.
CNNs have been shown to be very effective for image classification, and they have been used to achieve state-of-the-art results on many benchmark datasets. They are also being used for other tasks such as object detection, semantic segmentation, and natural language processing.
The other options are incorrect because they are not machine learning algorithms that are commonly used for image classification tasks. Decision trees are a type of supervised learning algorithm that can be used for classification or regression tasks. Naive Bayes is a type of probabilistic classifier that is based on Bayes’ theorem. K-means clustering is a type of unsupervised learning algorithm that can be used to cluster data points into groups.