APPLICATIONS OF IMAGE PROCESSING IN MACHINE LEARNING



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APPLICATIONS OF IMAGE PROCESSING IN MACHINE LEARNING

Image processing in machine learning

In today’s world Image processing and Machine learning together form amazing things.

Let’s know……

  • What is Image Processing?

Image processing is a method to perform operations on an image to extract information from it or enhance it, technically done by using complex algorithms. Here, the image is used as the input, where the useful information returns as the output. Digital image processing has a broad range of applications in the military, biomedical field, astronomy, artificial intelligence, scene analysis, robotics, etc.

  • What is Machine Learning?

Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. Machine learning is a subset of artificial intelligence, which focuses on using statistical techniques to build intelligent computer systems to learn from databases available to it.

Processing of an image is often an initial step to later extract the features that would be used to train a machine learning classifier.

In this article, we will be covering some Applications of image processing in machine learning.

Image analysis can be of great use in the healthcare industry. Computer vision software

based on deep learning algorithms is already making things more comfortable in the healthcare industry. Such software is making automated analysis possible to deliver more accurate results at a fast rate. When this technology is appropriately used, it helps us to reduce dependency on manual analysis. Machine learning (ML) plays an important role in the medical imaging field, including medical image analysis and computer-aided diagnosis.

Areas where image processing and machine learning can be applied, are as follows:

1.Pixel-based machine learning in medical imaging

Based on the most popular uses of Machine Learning i.e. Classification of the object, Recently, pixel-based ML (PML) emerged in medical image processing/analysis, which uses pixel/voxel values in images directly instead of features calculated from segmented objects as input information. thus, feature calculation or segmentation is not required.

2.Medical X-ray

In most hospitals around the world, radiologists are made to study the X-ray to search for anomalies. By making use of automated image analysis with advanced deep learning algorithms, the burden on the radiologists can be reduced, and more accurate and faster results can be obtained. Such analysis can help radiologists in taking appropriate decisions. As a result, Radiologists need to focus only on those reports in which image analysis marks as important.

It was challenging for the defence personnel to access some specific locations since they don’t know what lies ahead. The progression of image processing has revolutionized warfare effectively. Remote-controlled drones can now be used to capture perceptions of such places and later analysed using machine learning algorithms.

Monitoring cameras that give an alert when a person is near the door can be even made to understand who that person is. Image processing can make it happen and will change the world completely.

Self-driving cars are the eternity and are the greatest ever thing to happen in the industry. Self-driving cars do all the driving for us; we can do whatever we want. Self-driving cars work based on Object detection. Object detection involves image distribution and image localization. Image classification is identifying what the objects are in the image and image localization is about providing specific locations about this object.

In Agriculture Industry, Image Processing simultaneously with machine learning, can be defined as a game-changer in the new era. This helps in increasing the quality of any product.

To detect the weeds, we can use this method. Edge-based machine classifiers can detect these weeds.

Infrared image analysis assists in understanding and monitoring irrigation systems. Even the infrared image analysis can be used to prognosticate the harvest time.

Google Lens is one such reinforcement that delivers the use of deep machine learning to process complex images google Lens an app launched by Google, uses Image processing techniques along with AI technologies and deep machine learning can come to your redemption again. Google Lens identifies and explains what it flashes to give actions based on that.

How Image processing in Machine learning will change the world is not limited to the points discussed above. We are still at the beginning stages of image processing, and we are yet to identify the greatest potential. There are also lots of problems connected with the accommodation of vast data that is captured by cameras all around the world. We are still conveying lots of research and investigation to examine more about the inclinations of image processing.

References :

https://pubmed.ncbi.nlm.nih.gov/22481907/

https://www.ijcaonline.org/archives/volume181/number25/kathait-2018-ijca-917668.pdf

Shreyash Gadewar — Medium

Machine Learning in Python — PyImageSearch

Big data in healthcare: management, analysis and future prospects | SpringerLink

Aided Diagnosis — an overview | ScienceDirect Topics

Top 10 Applications of Machine Learning in Healthcare — FWS (flatworldsolutions.com)

792079.pdf (hindawi.com)

Wonders in Image Processing with Machine Learning (opendatascience.com)

A Gentle Introduction to Object Recognition With Deep Learning (machinelearningmastery.com)

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