Early Arthritis Detection Using Convolutional Neural Networks for Enhanced Diagnostic Accuracy.
Convolutional Neural Network using knee X-rays detected arthritis with 95% accuracy.
Early Arthritis Detection Using Convolutional Neural Networks for Enhanced Diagnostic Accuracy.
Arthritis is a widespread musculoskeletal disease, which is characterized by inflammation, pain, stiffness, and progressive loss of joint mobility and has a significant impact on quality of life across the globe.
The research seeks to create an automated mechanism of precise and timely identification of arthritis through the use of deep learning.
The framework based on the Convolutional Neural Network (Convolutional Neural Network) was suggested to support automated detection of arthritis by using knee X-ray images.
The confusion matrix and ROC analysis indicated a high level of discriminative ability in differentiating arthritic and normal knee conditions.
Grad- CAM visualizations were used successfully to highlight relevant areas in radiographs that have been used to predict the model.
DISCUSSION: The results show that deep learning-based analysis of knee radiographs can offer reliable and efficient detection of arthritis.
The interpretability methods, like Grad-CAM, improve trust and transparency in model predictions that can be more applicable to clinical practice.
This paper has shown that a Convolutional Neural Network-based system could be used to identify arthritis based on knee X-rays with high accuracy and reliability.
The proposed system can be used to support clinicians in musculoskeletal imaging workflows and can be integrated into computeraided diagnostic systems to assist in the early screening and better patient care.