From Facebook, Instagram, and Twitter to other social media platforms, instant image sharing is a regular phenomenon - it's important to get our tech-ready to talk the language of images. While it is simple for human brains to understand an image's context, significance, and correlations, programming a machine to do the same is challenging. 

In order to interpret images, computers treat them as 2D arrays of numbers. For example, when colour is added, it transforms into a 3D array. Their task is to use a standard image as an input and produce a classification output that resembles the functions of the human brain. This is how the birth of Convolutional neural networks (CNNs) took place.

Computer vision is a field with an agenda to view the world just as humans do. Now, the advancements in Computer Vision with Deep Learning have been developed and optimised with time, primarily over one particular algorithm — a Convolutional Neural Network. 

• In their work from 1980, Kunihiko Fukushima and Yann LeCun set the groundwork for convolutional neural network research. 
• Again, one of the turning points in this domain came in 2012 when Alex Krizhevsky won the ImageNet competition. He used the artificial neural network to bring down the image classification error from 26 per cent to 15 per cent - a substantial drop.

Since then, businesses such as Google, Facebook, Pinterest, Instagram, and others have started using CNNs to accelerate their business growth. As a result, a lot of applications emerged that people encounter daily. Here, we have curated the top five applications of CNNs:

Facial recognition

Face detection in photos has been accomplished using CNNs. After receiving an image as input, the network outputs a set of values that indicate the attributes of faces or facial features at various points in the image. They can accurately and readily identify facial features like the eyes, nose, and mouth while minimising distortions brought on by angles or shadows. The process includes:

• Identification every face in the picture
• Focus on each face despite external disturbances such as light, pose, angle, etc.
• Identify unique facial features
• Compare collected data with the existing ones in the database to match a face with a name.

Take, for example, facial detection paves the way for more alterations and manipulations. Snapchat and Facebook Messenger filters are the most known examples. The filters add new components or effects in place of the face's basic, automatically created layout.

Medical imaging

In medical imaging, CNN is valuable in better accuracy in identifying tumours or other anomalies in X-ray and MRI images. Based on previously processed similar images by CNN networks, CNN models may analyse an image of a human body part, such as the lungs, and pinpoint where there might be a tumour and other anomalies like broken bones in X-ray images. Similarly, medical images like CT scans and mammograms can be used to diagnose cancer. In order to determine whether any indicators within a picture indicate malignancy or damage to cells owing to both hereditary and environmental factors, such as smoking habits, CNN models compare the image of a patient with database images that include comparable features.

Document analysis 

Document analysis can also make use of convolutional neural networks. This has a significant impact on recognisers in addition to being helpful for handwriting analysis. A machine must process approximately a million commands per minute to scan someone's writing and compare it to its extensive database. By identifying words and phrases associated with the subject of a given document, CNN networks can use both text and visuals to comprehend better what is written within.

Autonomous driving

Images can be modelled using convolutional neural networks (CNN), which are used to model spatial information. CNNs are regarded as universal non-linear function approximators because of their superior ability to extract features from images such as obstacles and interpret street signs. Furthermore, as the depth of the network grows, CNNs may detect a variety of patterns. For instance, the network's initial layers will record edges, but its deeper layers will capture aspects like an object's shape that are more complicated (leaves in trees or tyres on a vehicle). As a result, CNNs are the primary algorithm in self-driving cars.

Biometric authentication

By identifying specific physical traits connected to a person's face, CNN has been utilised for biometric identification of user identity. CNN models can be trained on people's images or videos to identify particular face traits like the space between the eyes, the nose's shape, the lips' curvature, etc. CNN models have also recognised various emotional states such as happiness or sadness based on photos or videos of people's faces. CNNs can also assess whether a subject is blinking in a photo and the general form of multiple-frame facial images.