You are using Artificial Intelligence (AI) solutions right now, even if you don’t know it. Image recognition is one of its main uses. Science, business or education are fields benefiting from this application, but medicine is probably one of the most interesting.
We are producing more and more data every day. To be precise, we produce 2.5 quintillion bytes every day. Amazing, isn’t it? Such a huge volume of data, in conjunction with the development of Artificial Intelligence, is offering a wide range of opportunities and paths to different industries.
Deep Learning, a branch of Artificial Intelligence (we told you about the main differences between the branches of AI here), can be applied in as many sectors as you can imagine. For instance, some of its main applications are:
- Prediction: Artificial Intelligence allows computers to learn from past experiences (or data) and predict future events (without being explicitly programmed). In fact, predictions are the main application of AI. For example, Netflix can forecast what you, as an audience, are going to love, thanks to learning from the data of past experiences. But predictions based on past data can be applied to any field, such as medicine, where AI can be used to predict which patients have a higher risk of suffering a heart attack.
- Natural Language Processing: (NLP) allows humans to communicate effectively with computers and helps computers interpret and process the human language. NLP draws from many disciplines, including computer science and computational linguistics. The goal of NLP is to process raw language inputs and, thanks to linguistics and algorithms, operate with the text. Therefore, considering the amount of unstructured data that is generated every day, from medical records to social media, automation is a must to fully analyze text and speech data efficiently. As stated in this analysis (O. Davydova), Natural Language Processing helps us obtain automatic translations, interactions, text classifications, or speech-to-text and text-to-speech conversions, just to name a few examples.
- Image Recognition: this is the core technology used by autonomous cars. Only this fact is noteworthy per se, but image recognition is being used in many other ways. Most of the time, users are aware that AI is the technology behind some actions, such as automated image organization of large databases and visual websites, as well as face and photo recognition on social networks, such as Facebook, or photo editing, like adding creative filters to users’ pictures, as in the case of Snapchat.
Other things one can do thanks to image recognition are:
- Eye-tracking: enables recording of eye position and movement, analyzing how humans process visual information. It records attention, interest, and arousal.
- Iris recognition: it is being used as the new fingerprint at airports, security, etc.
- Boosting augmented reality: pointing with your camera at an object can let you know all information about the object, no matter how complex it is (for example, a reactor control unit).
- Data from image: thanks to image recognition, there are solutions that extract data from images and store it, making it easy to recover it later using keywords or locations, to name a few.
However, image recognition is applied in other areas, not only in social networks or websites. For instance, it is the main system behind autonomous cars, as mentioned above. Image recognition enhances security and effectivity in this type of cars and detects things on roads, such as pathways, moving objects, vehicles, and people, but also predicts speed, location, and behavior of other objects near the car. Some systems can even see in the dark.
Deep Learning can detect things in mammograms, scanners or many other medical tests. Medicine is one of the sectors in which DL shows its most interesting applications.
Using the same approach to detect obstacles on roads, DL can detect things in mammograms, scanners or many other medical tests. In fact, medicine is one of the sectors in which Deep Learning shows its most interesting applications:
- Tumor detection: early tumor detection allows early treatment. According to some research studies, “to detect a tumor, the DL algorithm learns important features related to the disease from a group of medical images and then makes predictions (i.e. detection) based on what it has learned”. Lung scans and melanoma images are some of the main areas of research in relation to the application of image recognition.
- Tracking tumor development: when the tumor has been identified, DL-based systems are an interesting option to track the development of tumors. Shape, area, density and location of the tumor are some of the characteristics analyzed by image recognition systems, which helps track tumor changes.
- Blood flow quantification & visualization: blood can be quantified without the use of contrast agents, allowing an efficient visualization and quantification of blood flow. This visualization of the blood flow (also inside the heart) can be used to conduct a comprehensive diagnosis of cardiovascular diseases.
This is just an overview of how Image Recognition systems based on Deep Learning can be applied to medicine and disease detection. You can now build your own systems and learn more about it.
If you also would like to help others in the detection of tumors and wish to innovate, helping in the development of medicine … this is your opportunity.
Join the #AI4Good hackathon organized by Terminus7 and sponsored by IBM, where you will have the chance to develop Deep Learning and Machine Learning models to detect Diabetes and breast tumors. In addition, you can win amazing prizes. Sign up here!
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