Machines are getting trained through computer vision which is helping them to recognize the objects with in-depth analysis of their various attributes. And computer vision is now highly used in AI and ML-based project developments.
What is Computer Vision in Machine Learning and AI?
Computers play an excellent role in helping machines understand the different types of high-volume structural tasks performed by humans. In healthcare medical imaging analysis, it assists machines looking by breaking down images into atomic components and analyzing the same based on prior indexing.
And this process is done by training the neural networks with a huge number of images in the process through machine learning. And a fully functional machine learning model can recognize such images when presented with images, similar to training data sets.
A neural network algorithm in healthcare can identify if there are any kind of health risks, with the best level of accuracy.
Computer vision is mainly used to perform two different tasks. First – Object identification, in which objects are segregated new inputs in existing classes for which its has been trained. Secondly – Computer vision is used for object identification to differentiate between two items belonging to the same group.
Healthcare is a key sector; computer vision is playing a bigger role in analyzing the medical images and diagnosis various types of diseases accurately.
And with more improvements in ground-braking technology, it is going to improve the medical imagining analysis for accurate predictions. So, right here we will learn how accuracy for computer vision for different types of medical imaging will improve.
X-rays are one of the most used medical images in hospitals to identify the abnormalities, organ damage of the human body, or diagnoses the diseases. Here computer-based vision can be trained to classify the scanned results as radiologist doctors do and diagnosis the potential health problems in a single view.
And with the more accurate training data that is created through medical image annotations to make the computer vision learn similar patterns and train the machine learning algorithm to learn and predict in the near future. And it is possible only when each x-ray image is precisely annotated for computer vision training.
This is one of the most widely used imaging techniques utilized to diagnose the abdomen of people for kidney, liver, and other organ functions. This imaging technique is also used for pregnant women for fetus checkups and diagnosis if any kind of complications can arise in near future to take health concerning decisions.
Using the huge amount of ultrasound images to train the medical imaging application, a computer-vision ultrasound system can show more comprehensive results with accuracy, than usually analyzed by radiologists.
And if such models are trained with more accurate data, it will significantly enhance the level of accuracy in medical imaging analysis through machine learning.
CT scans show a more detailed pictorial representation of body parts like the brain, chest, and other parts of the body. It helps to detect tumors, internal bleeding in the brain, or other organs and diagnosis any potential conditions of life-threatening diseases.
Here, if computer vision-based technology is used to analyze such images with a fully automated process, the precision level of prediction will increase. And using machine-based technology to identify such illness is that it can detect the details at minute levels which is not easily visible to human eyes.
As per the recent research and studies at the University of Central Florida, the accuracy level of detecting lung cancer through such machines is 95% compare to well-trained and experienced doctors, who have an accuracy of only 65% in detecting similar types of diseases. And results such predictions become more critical when used for diagnosing strokes, brain damage, or internal bleeding.
Magnetic resonance imaging (MRI) is one of the most advanced levels of medical imaging analysis to detect various health problems like bone structure, problems in softer tissues, or joints, and the circulatory system with better details.
Using the accurate image annotation techniques like semantic segmentation or polygons, machines are trained through computer vision to identify the clogged blood vessels and cerebral aneurysms among the patients diagnosed.
And with the time being, more qualitative and quantitative machine learning training data, the computer vision accuracy will improve resulting in more precise diagnosis through medical imaging will improve the overall healthcare services.
Other Benefits of Using Computer Vision
The use of computer vision-based technology in medical imaging diagnosis and analysis will help patients to timely get to know about life-threatening diseases. The benefit is that a well-timed diagnosis of deadly maladies like cancer will not only help to save lives but also help spot the slightest abnormalities among such patients.
And another advantage of using computer vision-based technology is, it helps to save additional tests and treatments incurred by the patients if the diagnosis is wrong, which also psychologically affects the patients and their families.
And the best part of using computer vision in healthcare is machine learning algorithms can be also reused for other patients or data from other hospitals can easily be transferred to train the machine learning algorithms to improve the accuracy level.
The Challenges with Computer Vision-based Training
Finding the right data sets and relevant images for training the computer vision-based technology is the real challenge. To get accurate data, such training data sets should be labeled with the right tagging and annotations to make computer vision accurately recognize the object of interest and predict with the right results.
Data privacy and personal security are other factors that restrict access to such data. But with proper data management and anonymization techniques, patient’s data can save the lives of many patients across the world.
However, relying fully on automated diagnosis and treatment processes is not possible right now, as there are multiple technical barriers. But with the consistent improvement in technology over the last few years and further, with more perfectly trained AI-enabled machines the medical image analysis will improve with groundbreaking results.