Prediction Model Using Reinforcement Deep Learning Technique for Osteoarthritis Disease Diagnosis

Abstract

Osteoarthritis is the most common class of arthritis that involves tears down the soft cartilage between the joints of the knee. The regeneration of this cartilage tissue is not possible, and thus physicians typically suggest therapeutic measures to prevent further deterioration over time. Normally, bringing about joint replacement is a remedial course of action. Expose itself in joint pain recognized with a normal X-ray. Deep learning plays a vital role in predicting the early stages of osteoarthritis by using the MRI pictures of muscles of the knee muscle. It can be used to accurately measure the shape and texture of biological structures can be measured consistently from X-ray images. Moreover, deep learning-based computation can be used to design framework to predict whether a given patient will develop osteoarthritis. Such a framework can identify clear biochemical changes in the focal point of ligaments of the knees of patients who have exhibit pre-indications in standard imaging. This study proposes framework to identify cases of osteoarthritis by using deep learning and reinforcement learning. It can be used as a clinical mechanism to predict the occurrence of osteoarthritis so that patients can benefit from early intervention.

A New Distributed Log Anomaly Detection Method based on Message Middleware and ATT-GRU

“…In addition, introducing the attention mechanism can improve the training efficiency of the neural network. Compared with the detection method based on LSTM [4]. GRU [5] has a simpler structure, fewer training samples, which is lightweight and easy to implement.…”