Edge Processing in Machine Learning for Arthritis Detection and Classification

Arthritis is a persistent inflammatory disease that affects the joints. It is a major global health issue, especially for people over 40. However, arthritis frequently remains untreated in isolated locations with poor access to medical services, impairing mobility and causing excruciating agony for those who suffer from it. This programme, which is in line with the National Programme for the Health Care of the Elderly (NPHCE), focuses on the diagnosis and classification of arthritis to enhance medical care for this population. To tackle this urgent matter, It is suggested to create a tool that can identify and categorize arthritis in isolated locations. The device uses sophisticated image processing techniques and machine learning algorithms to effectively identify various types of arthritis by utilising inputs such as MRIs, blood tests, and X-rays. For testing and training purposes, classes are named and labelled through repeated processing of large datasets. To improve arthritis classification accuracy, the research further develops the CNN algorithm. It also tests the model on arbitrary datasets and plots accuracy metrics to assess its effectiveness. The ultimate purpose is to demonstrate how well the created tool can identify and categorise cases of arthritis, guaranteeing prompt medical care for those in need and achieving the NPHCE's goals.