A Convolutional Neural Network is a type of artificial neural network designed to recognize images. It was first used in the 1980s, but it wasn’t until 2012 that Google researchers created a version that could be used for image recognition. A convolutional neural network is one of the most powerful tools available for object recognition because it can help computers interpret images.
How CNN works?
As the name implies, they are based on convolutional layers, which allow them to analyze images in much the same way that human vision works. In simple terms, they can be used to recognize objects and features within an image. CNNs often work best when performing classification tasks — identifying whether an image contains one thing or another — but can also be used for things like detection and segmentation. They’re particularly good at picking out patterns from large amounts of visual data.
Layers in CNN
A CNN is composed of multiple layers, each one performing a different task:
Convolutional Layer – Performs convolutions to extract features from the input images
Pooling Layer – Reduces the dimensionality of output by applying pooling operation on it
Fully Connected Layer – Adds a fully connected layer to link individual neurons together
Output Layer – Finally, adds an output layer that will yield actual classifications for the dataset being processed.
Steps involved in CNN
The initial step is to feed the image’s pixels in the form of arrays to the neural network’s input layer. The hidden layers extract features by performing various calculations and manipulations. The convolution layer and pooling layer retrieve features from the image. Finally, the fully connected layer identifies the image’s object.