Successful implementation of the idea could also improve veterinary medicine, as it could be widely used in clinical practice. And most importantly, to get the best possible sensitivity to correctly diagnose all the affected dogs. Osteoarthritis is the most common form of arthritis in dogs and is a degenerative joint disorder that leads to a permanent deterioration of the cartilage that provides cushioning for the joints. This painful condition is estimated to affect about a quarter of all dogs throughout their lifetime. Thus we wanted to employ computer science, AI models and and computer-aided detection to recognise the patterns and enable more accurate diagnosis in predictive analytics.
What are deep neural networks and deep learning model?
Deep learning systems are a part of machine learning methods, more specifically, a broader family based on artificial neural networks with representation learning. There are supervised, semi-supervised or unsupervised machine learning models.
When we consider what are deep neural networks in deep learning algorithms, the simple explanation would be that it is a neural network with more complexity, i.e. more than two layers. They employ sophisticated mathematical modelling to process data in complex ways.
We distinguish three main types of deep neural networks in machine learning:
Deep neural network can mean any learning neural network with multiple hidden layers. Deep convolutional neural networks are especially useful in image recognition.
Recurrent neural networks create a circular connection to allow some nodes to affect the inputs of other nodes in the network.
Artificial deep neural network models can aid in classification, regression problems, and sentiment analysiss and are designed to replicate the behavours observed in a human brain or an animal one. They allow the machine learning to process increasingly complex data via a simplified reflexion of the animal or human brain to mimic the behaviour of a nervous system and neurons which can aid in fields like medical imaging (e.g. magnetic resonance images).