CNN in Deep Learning: Layers, Applications, & Limitations
AI has seen a lot of growth in recent years and has managed to reduce the gap between the abilities of humans and machines. As technologies got more advanced over time with deep learning, a subfield of AI, its evolution was backed by CNN. Convolutional Neural Networks in deep learning is a class of deep learning that was introduced back in the 1980s and 1990s. In this blog, we will learn more about these neural networks, their applications, limitations, and more. So, let’s begin.
What is a Convolutional Neural Network?
A convolutional neural network helps analyze visual inputs. It differs from the basic neural networks that use matrix multiplications. Mathematically, we can explain it as a logical operation of two functions producing a third function that determines the modification caused in the shape of one by another.
In other words, the CNN algorithm in deep learning reduces the high dimensionality of visual inputs, making it a lot easier to process without losing any information.
How Convolutional Neural Networks Work?
A CNN comprises several layers, each learning to identify various attributes of an input image. Each image undergoes the application of a filter or kernel. As the layers progress, the complexity of the filters increases to examine and detect distinctive features characterizing the input object.
Consequently, the output of each convolved image, representing a partially recognized image at each layer, becomes the input for the subsequent layer. The method of convolution and filtering is replicated throughout numerous layers, supporting the CNN to learn different features at each layer. In the final layer, known as the fully connected (FC) layer, the CNN successfully identifies the image or the object.
Once trained, CNNs can organize new images, distinguish objects, recognize precise aspects, and segment images with high accuracy. To learn more about the working of CNN, you can opt for a comprehensive Deep Learning course.
Layers in Convolutional Neural Networks
The framework of CNN consists of the following four layers.
Convolutional Layer
The convolutional layer is an important building block of CNN as it carries the main portion of the network’s computational load. It is used to extract different features from the input images. Convolution is performed between an image and a filter or kernel of a certain size. When the filter is slid over the input image, a dot product is formed between the filter and the parts of the image with respect to the size of the filter.
We get an output in the form of a feature or activation map that has information about the image, such as corners and edges. The sliding size of the filter is called a stride. The map produced is then given to other layers for extracting more information about the image.
The convolutional layer is advantageous as it helps the computer to understand different aspects of an image, such as edges, curves, or textures. It also ensures the spatial relationship between the pixels is intact.
Pooling Layer
From the convolutional layer, the output is fed to the pooling layer. The main aim of the pooling layer is to combine and compress the learned features. It decreases the size of the convolved feature map to reduce computational costs. It does so by decreasing the connections between the layers and operating on each feature map independently.
There are different types of pooling operations available such as average and sum pooling. However, max pooling is one of the most popular operations. It reports the maximum output from the feature map.
Flattening Layer
From the pooling layer, the output of the image is received by the flattening layer. This layer is used to reshape the data so that it can be used in fully connected neural network layers. The two-dimensional array from the feature map (after the pooling layer) is converted into a single long continuous linear vector. It is then fed to the fully connected layer to classify the image. It enables models to handle high-dimensional input data, such as audio or image signals.
Fully Connected Layer
Fully connected layers, also known as hidden layers, are the last layers in CNN. They play a crucial role in independently identifying and categorizing images and consist of weights, biases, and neurons. It connects the neurons between the previous layer and the subsequent layer.
Here, the image is flattened and fed to a fully connected layer. The output goes through other fully connected layers to ensure better performance and reduce human supervision.
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Applications of Convolutional Neural Networks
Convolutional neural networks are used in real-world tasks of computer vision. Given below are some of the most commonly used applications of CNNs.
Object Detection
Objects can be identified based on their designs and patterns inside an image. CNN is trained to recognize a huge range of objects, such as pictures of day-to-day items, famous architectures, food, etc.
Facial and Emotional Recognition
CNNs are well-trained to identify faces. They can recognize facial features like nose, lips, eyes, and ears. The probability of facial recognition using CNN is near about 97%. They can also differentiate between various emotional expressions like sadness, happiness, anger, etc.
Automatic Translation
Translation between various languages, such as Spanish-English, English-German can be done through the use of CNN with precision and accuracy.
X-Ray Analysis
CNNs are used in the medical field to identify X-Ray images and detect abnormalities or tumors.
Biometrics
CNNs can be used to authenticate users by recognizing several facial features. Therefore, they are used in biometrics too.
Limitations of Convolutional Neural Networks
CNNs have caused a massive revolution in the field of AI. However, they have certain limitations. The following are some of them.
- Despite being trained with a large number of images and videos, they cannot completely remove inappropriate or unsuitable content.
- They may also fail under various circumstances and different angles to identify objects.
- CNN is limited to extracting visuals and tends to be slower.
- CNN needs a vast amount of dataset to analyze, process, and make predictions.
- CNN models take a long time to learn.
Conclusion
Despite their challenges and limitations, convolutional neural networks in deep learning are widely used for image classifications and computer vision tasks. They work by applying convolutional filters and detecting the images. They are crucial for deep learning and effective as they can learn independently from raw data.
FAQs
CNN is used to analyze the visual inputs. They are useful in finding patterns in images to recognize objects, classes, and categories.
Yes, CNN is a deep-learning algorithm used for analyzing visual imagery.
The three layers of CNN are convolutional, pooling, and fully connected layers. These layers learn to identify various attributes of an input image.
CNN is a supervised type of deep learning.
The two major steps of CNN are feature extraction and classification. Feature extraction is done through various layers of the network. These layers identify and extract relevant features from the input data. After the extraction, the fully connected layers process the features and make classifications or predictions.
The main process of CNN involves multiple layers detecting the features of an input image. A filter or kernel is applied to the image to produce an output that gets more detailed and better with every layer.
