Neural Networks
Neural Networks
A Neural Network is a processing device, either an algorithm whose design is inspired by the design and functioning of Brains. The Neural Networks, also known as Artificial Neural Networks or Neural Net. The Neural Network has the ability to learn by examples, which makes them flexible and powerful. For Neural Networks, there is no need to devise an algorithm to perform a specific task.
Artificial Neural Network
An Artificial Neural Network may be defined as an information processing model that is inspired by the way biological nervous system such as the brain, process information. ANNs possess large number of highly interconnected processing elements called units or neurons. Each neuron is connected with the other neurons by a connection link. Each connection link is associated with some weights which contain information about the input signal. This information is used by the neural net to solve a particular problem.
Biological Neural Network
It is well known that the human brain consists of a huge number of neurons with numerous interconnections.
Biological Neuron consists of three main parts:-
- Cell Body
- Dendrites
- Axon
Advantages of Neural Networks
- Adaptive Learning - An ANN is endowed with the ability to learn how to do the tasks based on the data given for training or initial experience.
- Self-Organization - An ANN can create its own organization or representation of the information it receives during learning time.
- Real-Time Operation - ANN Computations may be carried out in parallel.
- Fault Tolerance Via Redundant Information Along - Partial destruction of a neural network leads to the corresponding degradation of performance.
Applications of Neural Network
- Air Traffic Control
- Data Mining, Cleaning and Validation
- Echo Pattern
- Fraud Detection
- Medical Research
- Music Composition
- Weather Prediction
- Voice Recognition
Usecases of Neural Networks
?Possibilities for Neural Networks in Healthcare-
Though they may seem like a futuristic concept, ANNs have been used in healthcare for several decades. Before 2006, the main successes of ANNs were found in areas like speech processing and image processing. Today, as new technologies emerge, capable of changing the way that we approach neural networks in the first place – it’s worth noting that there may be numerous new options for changing the industry. Today, the possibilities for Neural Networks in Healthcare include:
- Diagnostic systems – ANNs can be used to detect heart and cancer problems, as well as various other diseases informed by big data.
- Biochemical analysis – ANNs are used to analyze urine and blood samples, as well as tracking glucose levels in diabetics, determining ion levels in fluids, and detecting various pathological conditions.
- Image analysis – ANNs are frequently used to analyze medical images from various areas of healthcare, including tumor detection, x-ray classifications, and MRIs.
- Drug development – Finally, ANNs are used in the development of drugs for various conditions – working by using large amounts of data to come to conclusions about treatment options.
Current Examples of Neural Networks-
Neural networks can be seen in most places where AI has made steps within the healthcare industry. For instance, in the world of drug discovery, Data Collective and Khosla Ventures are currently backing the company “Atomwise“, which uses the power of machine learning and neural networks to help medical professionals discover safer and more effective medicines fast. The company recently published its first findings of Ebola treatment drugs last year, and the tools that Atomwise uses can tell the difference between toxic drug candidates and safer options.
Similarly, options are being found that could insert neural networks into the realm of diagnostic. For instance, in 2014, Butterfly Networks, which are transforming the diagnostic realm with deep learning, devices, and the cloud, raised $100M for their cause. This organization currently works at the heart of the medicine and engineering sectors by bringing together world-class skills in everything from electrical engineering, to mechanical engineering, and medicine.
The Future of Healthcare-
Perhaps the most significant problem with ANNs is that the learned features involved when it comes to assessing huge amounts of data can sometimes be difficult to interpret. This is potentially why ANNs are more commonly used during situations wherein we have a lot of data to ensure that the observed data doesn’t contain too many “flukes”. Think of it this way – if you toss a coin three times and receive “tails” every time, this doesn’t mean that a coin only has a “tails” side. It just means that you need further evaluation and more testing to get a proper reading of probability.
ANNs are going to need some tweaking if they’re going to become the change that the healthcare industry needs. However, alongside new AI developments, it seems that neural networks could have a very important part to play in the future of healthcare.
Thank You!!