A Novel Approach for Detection of Hand Arthritis using Convolutional Neural Network

Arthritis is becoming a more common disease across the globe. Different types of arthritis diseases are affecting the human body, they are osteoarthritis, rheumatoid arthritis, and psoriatic arthritis, within which the osteoarthritis is considered as the most common disease. The recent advances in Artificial Intelligence (AI) will eventually result in a fully automated workflow, which outperforms human performance. Advanced neural networks can recognize objects present in the images and further the objects will be categorized more precisely and quickly than humans. This research study has attempted to implement the machine learning technique to classify and categorize the different stages of hand arthritis in patients. This research study also investigates how Magnetic Resonance Imaging (MRI) is used for the detection and classification of Osteoarthritis in a descriptive and comparative manner. Recognizing the precise segment of RA is a difficult task because human abilities will frequently limit the methods applied. The Convolutional neural network (CNN) is used to recognize multiple examples of hand reputation. The Convolutional Neural Network (CNN) has the ability to recognize human traits and is anticipating the sophistication of hand radiographs from a large dataset. As a result, a research discussion of various arthritis detection techniques, feature extraction techniques, and classification schemes has been conducted.