What Are Convolutional Neural Networks? A Look at Layers and Applications
Convolutional Neural Networks (CNNs) are a class of deep learning algorithms that are mainly effective for image and video recognition, classification, and processing. They have revolutionized the field of computer vision due to their ability to automatically and adaptively learn spatial hierarchies of features from input images. A specialized type of linear mathematical operation termed convolution gives CNN its name.
Layers of CNN
CNNs are a sub-category of artificial neural networks. They process structured grid data like images. Unlike traditional neural networks, CNNs use a special kind of layer called convolutional layers to capture spatial and temporal dependencies in data. When it comes to CNN, many essential layers are stacked sequentially to get summed up to create the architecture of CNN.
- Input Layer
The input layer holds the raw pixel values of the image, usually represented as a 2D array for grayscale images or a 3D array for colored images (with depth corresponding to color channels).
- Convolutional Layer
The convolutional layer is the core building block of CNN. It involves sliding a filter or kernel over the input data and computing dot products to produce a feature map. This operation captures local spatial features. It also implements a set of small and learnable filters (called kernels) to the input data, which extracts features. Every filter extracts certain features from the input data, like edges, textures, or more complex patterns. Multiple filters help capture various features. The output of this layer is called feature maps, which get impacted by the Stride. Padding helps in preserving the edge information. It adds zeros around the input matrix to control the spatial dimensions of the output.
- Pooling Layer
Pooling layers decrease the spatial dimensions of the feature maps while retaining the most crucial information. It also reduces computation and makes the model translation invariant. Max-pooling and average-pooling are some pooling techniques. Where max-pooling chooses the maximum value within a small region of the feature map, decreasing the size and introducing translational invariance and pooling helps reduce the computational complexity of the network by making the model robust enough for small shifts.
- Activation Layer
The activation Layer is applied elementwise to the feature maps after the convolution operation to introduce non-linearity into the model, which is helpful for the network to learn complex patterns. Rectified Linear Unit (ReLU) is the most common activation function that replaces negative values with zero and leaves positive values unaltered, introducing non-linearity into the model.
- Dropout Layer
The dropout layer is a regularization technique used to stop overfitting. It prevents neural networks from being highly dependent on neurons and features. During training, a fraction of randomly chosen neurons (usually set as a hyperparameter) is temporarily dropped out or ignored.
- Flatten Layer
The feature maps are usually flattened into a one-dimensional vector to match the between the convolutional/pooling layers and the fully connected layers.
- Fully Connected Layer
The Fully Connected (FC) layer has neurons, biases, and weights. It is often equivalent to traditional neural networks. It connects each neuron in one layer to the other neuron in the next layer. They help combine the features learned by convolutional and pooling layers to make final predictions for classification or regression tasks. It is placed before the output layer and decreases human supervision.
- Output Layer
The final layer connected layer in the CNN produces the output. The number of neurons in this layer depends on specific tasks. It follows the activation function for classification works to generate a probability distribution over class labels. For instance, a single neuron for binary classification or multiple neurons for multi-class classification.
Applications of CNN
CNN application becomes useful where computer vision is necessary. As there are so many applications, sometimes it becomes impossible to list down all. Many want to get help from CNN to build some of the best deep learning algorithms to solve practical problems. Some of the wide ranges of applications, mainly in the field of computer vision, include:
- Object Detection
With CNN, several advanced-level models such as R-CNN, Fast R-CNN, and Faster R-CNN are in the pipeline for several object detection models deployed, especially autonomous vehicles such as running self-driving cars, facial detection, and more.
- Optical Character Recognition
With the help of convolutional neural networks, it gets easy to digitally recognize handwritten or printed text from scanned documents, images, or other sources. It is later used downstream for performing the real-time translation.
| Read More: CNN in image analysis
- Healthcare & Medical Imaging
CNN consists of innovative software solutions that detect conditions, including detecting cancer cells in patients. They are also used for disease diagnosis and analyzing medical images with the help of X-rays, MRIs, and CT scans.
- Semantic Segmentation
In 2015, a group of researchers from Hong Kong built a CNN-related Deep Parsing Network to equip rich information into an image segmentation model. Researchers from UC Berkeley developed fully neural networks that enhance state-of-the-art semantic segmentation.
- Natural Language Processing (NLP)
Tasks like text classification, sentiment analysis, and language translation for processing text data as sequences of images apply to NLP.
- Law Enforcement
Several law enforcement firms are benefiting from CNN to conduct facial recognition on absconders who have modified their facial looks to hide from authorities.
- Social Media
CNN is also useful for identifying individuals, objects, and places in the users’ photographs.
End Notes
With continuous advancements in technology and methodologies, convolutional neural networks will remain a cornerstone in AI and ML. It is considered a remarkable innovation that has the power to become a super-smart tool and a step ahead in making computers smart.
