Applications of Convolutional Neural Networks(CNN)

Prashant Sharma 22 Aug, 2023 • 8 min read

This article was published as a part of the Data Science Blogathon

What is CNN?

Convolutional Neural Network is a type of deep learning neural network that is artificial. It is employed in computer vision and image recognition. This procedure includes the following steps:

  • OCR and image recognition
  • Detecting objects in self-driving cars
  • Social media face recognition
  • Image analysis in medicine

The term “convolutional” refers to a mathematical function that is created by integrating two different functions. It usually involves multiplying various elements to combine them into a coherent whole. Convolution describes how the shape of one function is influenced by another function. In other words, it is all about the relationships between elements and how they work together.

Key responsibilities Convolutional neural networks

  • Sort visual content (explain what they “see”)
  • Recognize the objects in the scenery (for example, eyes, nose, lips, ears on the face)
  • Form groups of recognized objects (for e.g., eyes with eyes, noses with noses)

Another prominent use of CNNs is in laying the groundwork for various types of data analysis.

CNN classifies and clusters unusual elements such as letters and numbers using Optical Character Recognition (OCR). Optical Character Recognition combines these elements into a logical whole. CNN is also used to recognize and transcribe spoken words.

CNN’s classification capabilities are used in the sentiment analysis operation.

Let us now go over the mechanics of the Convolutional Neural Network.

How Does Convolutional Neural Network work?

Convolutional Neural Network structure consists of four layers:

Convolutional layer

The convolutional layer is where the action begins. The convolutional layer is designed to discover image features. Usually, it progresses from the general (i.e., shapes) to specific (i.e., identifying elements of an object, recognizing the face of a certain man, etc.).

Rectified Linear Unit layer (aka ReLu)

This layer is considered as an extension of a convolutional layer. The goal of ReLu is to increase the image’s non-linearity. It is the technique of removing excess fat from a picture in order to improve feature extraction.

Pooling layer

The pooling layer is used to minimize the number of input parameters, i.e., to conduct regression. In other words, it focuses on the most important aspects of the information obtained.

Connected layer

It is a standard feed-forward neural network. It’s the last straight line before the finish line, where everything is already visible. It’s only a matter of time until the results are confirmed.

Applications of Convolutional Neural Networks predicted class

Image Source –

Applications of Convolutional Neural Networks

Image Classification – Search Engines, Social Media, Recommender Systems

The major use of convolutional neural networks is image recognition and classification. It is also the only use case involving the most advanced frameworks (especially, in the case of medical imaging).

The CNN picture categorization serves the following purposes:

  • Deconstruct an image and find its distinguishing feature. The system employs a supervised machine learning classification algorithm for this purpose.
  • Reduces the description of its important credentials. It’s done with the help of an unsupervised machine learning algorithm.

This method is used in the following fields:
Image tagging

The most basic type of image classification algorithm is image tagging. The image tag is a term or a phrase that describes the images and makes them easier to find. This method is used by big companies like Facebook, Google, and Amazon. It is also one of the fundamental elements of visual search. Tagging involves recognition of objects and even sentiment analysis of the image tone.

Visual Search

This method involves comparing an input image to the access database. Furthermore, the visual search evaluates the image and searches for other photos that have comparable credentials.

Recommender engines

Another field where image classification and object identification can be used is recommender engines. Amazon, for example, employs CNN image recognition to make suggestions in the “you might also like” area. The presumption is based on the user’s expressed behavior. The products are matched based on visual criteria, such as red shoes and red lipstick for a red outfit. Pinterest employs CNN image recognition in a novel way. The organization focuses on visual credentials matching, which results in simple visual matching enhanced by tagging.

Face Recognition RNN Applications include Social Media, Identification, and Surveillance

Face recognition deserves its own section. This subset of image recognition deals with more complex images. Such images could include human faces or other living beings such as animals, fish, and insects.

The distinction between straight image recognition and face recognition is based on operational complexity — the additional layer of work required.

  • The shape of the face and its features are recognized first, followed by basic object recognition.
  • The features of the face are then examined further to determine its essential credentials. For example, It could be the shape of the nose, the skin tone, and texture, or the presence of scars, hair, or other surface irregularities.
  • The sum of these credentials is then calculated into the image data perception of a specific human being’s appearance. This procedure entails studying a large number of samples that each present the subject in a different way. For instance, whether with or without sunglasses).
  • The input image is then compared to the database, and the system recognizes a specific face.

Face recognition is used in social media platforms such as Facebook for both social networking and entertainment.

  • Face recognition in social networking serves to streamline the often dubious process of tagging people in photos. This feature is especially useful when you need to tag through hundreds of images from a conference or when there are far too many faces to tag. So, if you’re planning to build a social network, keep this feature in mind.
  • Facial detection in entertainment lays the groundwork for further transformations and manipulations. The most notable examples are Facebook Messenger filters and Snap chat Looksery filters. The filters depart from the face’s auto-generated basic layout and add new elements or effects.

Facial recognition technology is gaining traction as a viable method of personal identification.

Face recognition cannot be used to verify a persona in the same way that fingerprints and legal documents can. In cases where there is limited information, face recognition can be useful in identifying the person. For instance, from surveillance camera footage or a covert video recording.

Medical Image Computing – Predictive Analytics, Healthcare Data Science

Healthcare is the industry where all of the cutting-edge technology is put to the test.

If you want to test the usefulness of a certain technology, try employing it in a healthcare setting. Image recognition is no exception.

The most fascinating image recognition CNN use case is medical image computing.

The medical image includes a whole lot of further data analysis that arises from initial image recognition.

CNN medical image classification detects anomalies in X-ray and MRI images with better accuracy than the human eye.

These systems can display the series of photos as well as the differences between them. This feature lays the groundwork for future predictive analytics.

Medical image classification is based on massive datasets such as Public Health Records. It serves as a training basis for the algorithms and patients’ confidential data and test results. They work together to create an analytical platform that monitors the current status of the patient and forecasts results.

Health Risk Assessment Using Predictive Analytics

In healthcare, saving lives is a top priority. And it is always advantageous to have the ability to predict the future. Because when it comes to patient care, you must be prepared for anything. The health risk assessment is an excellent demonstration.

Convolutional Neural Network Predictive Analytics is used in this field.

Here’s how CNN Health Risk Assessment works:

  • CNN uses a grid topology approach to process data, which is a set of spatial correlations between data points. The grid is two-dimensional in the case of images. The grid is one-dimensional in the case of time series textual data.
  • The convolution algorithm is then used to identify some aspects of the input.
  • Take into account the variations of input.
  • Determine variable interactions that are sparse.
  • Use the same settings for all of a model’s functions.

Health Risk Assessment applications are a broad category, so we’ll focus on the most notable:

  • HRA is a predictive application that computes the likelihood of specific events. Based on patient data, this use case includes disease progression or complications. It looks for similar PHR, analyses the patient’s data, looks for patterns, and calculates potential outcomes. This system can be used for routine health checks.
  • The framework can be expanded by including a treatment plan. In this case, the prediction determines the best way to treat the symptoms.
  • The HRA system can also be used to investigate the specific environment and identify potential hazards for those who work there. This method is used to assess dangerous situations. In Australia, for example, officials are studying sun activity to determine the level of radiation threat.

Drug Discovery Using Predictive Analytics

Another major healthcare field that makes extensive use of CNNs is drug discovery. It is also one of the most inventive uses of convolutional neural networks in general.

RNN (Recurrent Neural Network) and stock market prediction are examples of pure data tweaking, whereas drug discovery and CNN are not.

The problem is that drug discovery and development is a time-consuming and costly process. In drug discovery, scalability and cost-effectiveness are critical.

The process of developing new drugs lends itself well to the implementation of neural networks. During the development of a new drug, there is a large amount of data to consider.

The following stages are involved in the drug discovery process:

  • This is a clustering and classification problem involving the analysis of observed medical effects.
  • Machine learning anomaly detection may be useful in hit discovery. The algorithm searches the compound database for new activities that can be used for specific purposes.
  • Then, using the Hit to Lead process, the results are narrowed down to the most relevant. That’s what dimensionality reduction and regression are all about.
  • Then there’s Lead Optimization, which is the process of combining and testing lead compounds to find the best approaches to them. The stages involve the examination of the organism’s chemical and physical effects.

Following that, the development shifts to live testing. Machine learning algorithms were relegated to the background and were used to structure incoming data.

CNN optimizes and streamlines the drug discovery process at critical stages. It allows for a reduction in the time required to develop cures for emerging diseases.

Precision Medicine Using Predictive Analytics

A similar approach can be used with existing drugs when developing a treatment plan for patients. Precision medicine aims to find the most effective way to treat a disease.

Supply chain management, predictive analytics, and user modeling are all part of precision medicine.

This is how it works:

  • From the standpoint of data, the patient is a collection of states that are affected by a variety of factors (symptoms and treatments).
  • The addition of variables (treatment types) has specific effects in both the short and long term.
  • Each variable has its own set of statistics regarding its impact on a symptom.
  • Data is combined to form an assumption about the best course of action based on the available information.
  • The various outcomes and changes in the patient’s condition are then considered. This is how the assumption is validated. This stage is handled by recurrent neural networks because it necessitates the analysis of data point sequences.

Frequently Asked Questions

Q1. What are the applications of CNN?

A. Convolutional Neural Networks (CNNs) excel in image and video analysis tasks due to their hierarchical feature extraction. They find applications in image recognition, object detection, facial recognition, medical image analysis, self-driving cars, and more. CNNs leverage their convolutional and pooling layers to automatically learn relevant features, making them pivotal in visual data processing tasks.

Q2. What are benefits and application of CNN?

A. Convolutional Neural Networks (CNNs) offer automatic feature extraction from images, reducing manual preprocessing. They excel in image-related tasks like object recognition, scene understanding, and medical image analysis. CNNs can capture spatial hierarchies in data due to their convolution and pooling layers. This makes them valuable for tasks requiring complex visual pattern recognition and has revolutionized fields like computer vision and AI-driven image analysis.


Convolutional Neural Networks reveal and describe hidden data in an understandable manner.

Even in their most basic uses, neural networks demonstrate how much can be accomplished with their assistance. The manner in which CNN recognizes photographs reveals a great deal about the composition and execution of the visuals. Convolutional Neural Networks, on the other hand, uncover novel medications, which is just one of many amazing examples of how artificial neural networks are making the world a better place.

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Prashant Sharma 22 Aug 2023

Currently, I Am pursuing my Bachelors of Technology( B.Tech) from Vellore Institute of Technology. I am very enthusiastic about programming and its real applications including software development, machine learning and data science.

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