Machine Learning and neural networks are expanding our understanding of data and the insights it holds. From a business standpoint, neural networks are engines of generating opportunities. They make sense of data and let you enjoy it.

Convolutional Neural Networks holds a special place in that regard. The development and implementation of Convolutional Neural Networks show us:

how many different insights are behind visual content;

how data impact customer satisfaction.

In this article, we will explain what CNN is, how it operates, and look at its common business cases.

What is CNN?

Convolutional Neural Network is an artificial deep learning neural network. It is used for computer vision/image recognition. This process includes the following operations:

Image recognition and OCR

Object detection for self-driving cars

Face recognition on social media

Image analysis in healthcare

The term “convolutional” means mathematical function derived by integration from two distinct functions. It includes rolling different elements together into a coherent whole by multiplying them. Convolution describes how the other function influences the shape of one function. In other words, it is all about the relations between elements and their operation as a whole.

The primary tasks of convolutional neural networks are the following:

Classify visual content (describe what they “see”),

Recognize objects within is scenery (for example, eyes, nose, lips, ears on the face),

Gather recognized objects into clusters (for example, eyes with eyes, noses with noses);

The other prominent application of CNNs is preparing the groundwork for different types of data analysis.

CNNs use Optical Character Recognition (OCR) to classify and cluster peculiar elements like letters and numbers. Optical Character Recognition put these elements together into a coherent whole. Also, CNN is applied to recognize and transcribe the spoken word.

The sentiment analysis operation uses the classification capabilities of CNN.

Now, let’s explain the mechanics behind the Convolutional Neural Network.

How Does Convolutional Neural Network work?

Convolutional Neural Network architecture consists of four layers:

Convolutional layer - where the action starts. The convolutional layer is designed to identify the features of an image. Usually, it goes from the general (i.e., shapes) to specific (i.e., identifying elements of an object, the face of certain man, etc.).

Then goes Rectified Linear Unit layer (aka ReLu). This layer is an extension of a convolutional layer. The purpose of ReLu is to increase the non-linearity of the image. It is the process of stripping an image of excessive fat to provide a better feature extraction.

Pooling layer is designed to reduce the number of parameters of the input i.e., perform regression. In other words, it concentrates on the meaty parts of the received information.

The connected layer is a standard feed-forward neural network. It is a final straight line before the finish line where all the things are already evident. And it is only a matter of time when the results are confirmed.

Let’s explain how CNN works in case of image recognition.

CNN perceives an image as a volume, a three-dimensional object. Usually, digital color images contain Red-Blue-Green, aka RGB encoding. What it means is that convolutional networks understand images as three distinct channels of color stacked on top of each other.

CNN groups pixels and processes them through a set of filters designed to get certain kinds of results. For example, to recognize geometrical shapes on an image). The number of filters applied usually depends on the complexity of an image and the purpose of recognition.

Pooling layer is designed to reduce the number of parameters of the input, i.e., perform regression.

As a result, you can a recognized image by identifying credentials and data layout that represents a blueprint of a picture of a specified kind.

Now let’s take a look at the most prominent business applications of CNNs.

Business applications of Convolutional Neural Networks

Image Classification - Search Engines, Recommender Systems, Social Media

Image recognition and classification is the primary field of convolutional neural networks use. It is also the one use case that involves the most progressive frameworks (especially, in the case of medical imaging).

The purpose of the CNN image classification is the following:

Deconstruct an image and identify its distinct feature. For that, the system uses supervised machine learning classification algorithm.

Reduce the description of its essential credentials. It is done by unsupervised machine learning algorithm )

The following fields are using this process:

Image tagging algorithms are the most basic type of image classification. The image tag is a word or a word combination that describes the images and makes it easier to find. Google, Facebook, and Amazon are using this technique. It is also one of the foundation elements of visual search. Tagging includes recognition of objects and even sentiment analysis of the picture tone.

Visual Search – this technique involves matching an input image with the available database. Besides, the visual search analyzes the image and looks for images with similar credentials. For example, this is how Google can find versions of the same model but in different sizes.

Recommender engines is another field to apply image classification and object recognition. For example, Amazon uses CNN image recognition for suggestions in the “you might also like” section. The basis of the assumption is the user’s expressed behavior. The products itself are matched on visual criteria — for example, red shoes and red lipstick for the red dress. Pinterest uses image recognition CNN is a different way. The company relies on the visual credentials matching, and this results in a simple visual matching supplemented with tagging.

Face Recognition Appications of RNN is Social Media, Identification procedures, Surveillance

Face recognition deserves a separate mention. This subdivision of image recognition comprehends more complex images. Such images might include human faces or other living beings, animals, fish, and insects included.

The difference between straight image recognition and face recognition lays in operational complexity — the extra layer of work involved.

First goes basic object recognition - the shape of the face and its features are recognized.

Then the features of the face are further analyzed to identify its essential credentials. For example, it can be the shape of the nose, its skin tone, texture, or presence of scar, hair or other anomalies on the surface;

Then the sum of these credentials is calculated into the image data perception of the appearance of a particular human being. This process involves studying many samples that present the subject in a different form. For example, with or without sunglasses).

Then the input image is compared with the database, and that’s how the system recognizes a particular face.

Social media like Facebook use Face recognition for both social networking and entertainment.

In social networking , face recognition serves as a streamlining of the often dubious process of tagging people in the photo. This feature is especially helpful when you need to tag through a couple of hundred images from the conference, or there are way too many faces to tag. So if you are going to build your social network , think about this feature.

In entertainment , face recognition lays the groundwork for further transformations and manipulations. Facebook Messenger’s filters and Snapchat Looksery filters are the most prominent examples. The filters jump from the autogenerated basic layout of the face and attach new elements or effects.

Facial recognition technology is establishing itself as a viable option for personal identification.

Face recognition can’t serve as a verification of the persona on par with fingerprints and legal documents. Face recognition is constructive in identifying the person in cases of limited information. For example, from the surveillance camera footage or sneak video recording.

Legal, Banking, Insurance, Document digitization - Optical Character Recognition

Optical Character Recognition was designed for written and print symbols processing. Like face recognition, it involves a more complicated process with move moving parts.

In its core, OCR is a combination of computer vision with natural language processing. First, the image is recognized and deconstructed into characters. Then, the characters are extracted together into a coherent whole.

Here’s how it works:

The first step , there is image recognition involved. The image is scanned for elements that resemble written characters (it can be specific characters or in general).

The second step , each character is broken down to critical credentials that identify it as such (for example, a particular shape of letters “S” or “Z.”)

The third step , the image is matched with the respective character encoding.

The fourth step , the recognized characters are compiled into the text according to the visual layout of an input image.

Image tagging and further descriptions of the image content for better indexing and navigation are using CNN. The eCommerce platforms, such as Amazon, are using it for a more significant impact.

The legal organizations, as banking and insurance, use Optical Character Recognition of handwriting.

The recognition of personal signature becomes an extra validating and verifying layer. The process resembles face recognition bar the generalization.

like the face, a signature contains unique features that make it distinct from the others.

Signatures contain a minimal amount of generic elements with unique credential data. For example, the infamous Donald Trump “demon screaming” signature.

The system concentrates on the particular sample and the credentials of the specific person signature.

But, the first use case of Optical Character Recognition is digitizing document and data.

The formatting of the text plays a significant role, as it is crucial to transcribe the document’s content. OCR algorithms reference the document templates. It means the whole operation resembles an elaborate “connect the dots” game.

Medical Image Computing - Healthcare Data Science / Predictive Analytics

Healthcare is the industry where all the cutting edge technologies get their trial on fire.

If you want to determine the practical worth of a particular technology - try using it for some healthcare purposes. Image recognition is no different.

Medical Image Computing is the most exciting image recognition CNN use case.

The medical image involves a whole lot of further data analysis that spurs from initial image recognition.

CNN medical image classification detects the anomalies on the X-ray or MRI images with higher precision than the human eye.

Such systems can show how the sequence of images and the differences between them. This feature prepares the grounds for further predictive analytics.

Medical image classification relies on vast databases that include Public Health Records. It serves as a training basis for the algorithms and patients private data and test results. Together they make an analytical platform that keep an eye on the current patient state and predict outcomes.

Predictive Analytics - Health Risk Assessment

Saving lives is a top priority in healthcare. And it is always better to have a power of foresight at hand. Because when it comes to handling the patient treatment, you need to be ready for anything.

A case in point is the health risk assessment.

This field is the one where Convolutional Neural Network Predictive Analytics are applied.

Here’s how Health Risk Assessment CNN works:

CNN process data with a grid topology approach - a set of spatial correlations between data points. In the case of images, the grid is two-dimensional. In case of time series textual data - the grid is one-dimensional.

Then the convolution algorithm is applied to recognize some aspects of input;

Take into consideration the variations of an input ;

Determine sparse interactions between variables;

Apply same settings for many functions of a model

Health Risk Assessment applications are a broad term, so let’s explain the most prominent:

HRA is a predictive application that calculates the probability of certain events. This use case includes disease progression or complications happening based on patient data. It matches similar PHR, analyzes the patient’s data, finds patterns, and calculate possible outcomes. Routine health checks can enjoy using this system;

The framework can expand by adding the treatment plan. In this case, the prediction determines the optimal way of symptoms treating.

HRA system also can be used to study the specific environment and explore possible risks for people working there. The assessment of dangerous situations is using this approach. For example, in Australia, the officials are studying sun activity and determine the level of radiation threat.

Predictive Analytics - Drug Discovery

Drug discovery is another major healthcare field with the extensive use of CNNs. It is also one of the most creative applications of convolutional neural networks in general.

Like RNN (Recurrent Neural Network) and stock market prediction, drug discovery, and CNN is pure data tweaking.

The thing is - drug discovery and development is a lengthy and expensive process. Scalability and cost-effectiveness are essential in drug discovery.

The very method of creating new drugs is very convenient for the implementation of neural networks. There are a lot of data to take into consideration during the development of the new drug.

The process of drug discovery involves the following stages:

Analysis of observed medical effects - this is a clustering and classification problem.

Hit discovery - that’s where machine learning anomaly detection may come in handy. The algorithm goes through the compound database and tries to uncover new activities for specific purposes.

Then the selection of results is narrowed down to the most relevant via Hit to Lead process . That’s dimensionality reduction and regression.

Next goes Lead Optimization - the process of combining and testing the lead compounds and finding the most optimal approaches to them. The stages involve the analysis of chemical and physical effects on the organism.

After that, the development shifts in living testing. Machine learning algorithms took a back seat and used to structure incoming data.

CNN streamlines and optimizes the drug discovery process on the critical stages. It allows compressing the timeframe for the development of cures for emerging diseases.

Predictive Analytics - Precision Medicine

The similar approach also can be used with the existing drugs during the development of a treatment plan for patients. Precision medicine was designed to determine the most effective way of treating the disease.

Precision medicine includes supply chain management, predictive analytics, and user modeling.

Here’s how it works:

From the data point of view, the patient is the set of states that depend on a variety of factors (symptoms and treatments).

The addition of the variables (types of treatment) causes specific effects in short and long term perspectives.

Each variable has its own set of stats about its effect on a symptom.

Data is combined to create an assumption of what is the best course of action according to the available information.

Then various results and changes in the patient’s state are put into perspective. That’s how the assumption is verified. Recurrent neural networks handle this stage as it requires the analysis of the sequences of the data points.

Conclusion

Convolutional Neural Networks uncover and describe the hidden data in an accessible manner.

Even in its most basic applications, it is impressive how much is possible with the help of a neural network.

The way CNN recognize images says a lot about the composition and execution of the visuals. But, Convolutional Neural Networks also discover newer drugs, which is one of the many inspiring examples of artificial neural networks making the world a better place.

CNN creates the way we see the world and operate within it - think about how many times you’ve met an interesting person because of the tag on the photo? Or how many times you’ve found the thing you’ve been looking for via Google’s visual search.

That’s all Convolutional Neural Networks in action.