HYBRID AI MODEL FOR THE DETECTION OF RHEUMATOID ARTHRITIS FROM HAND RADIOGRAPHS

Abstract

The study aims to develop a computerized hybrid model using artificial intelligence (AI) for the detection of rheumatoid arthritis (RA) from hand radiographs. The objectives of the study include (i) segmentation of proximal interphalangeal (PIP), and metacarpophalangeal (MCP) joints using the deep learning (DL) method, and features are extracted using handcrafted feature extraction technique (ii) classification of RA and non-RA participants is performed using machine learning (ML) techniques. In the proposed study, the hand radiographs are resized to [Formula: see text] pixels and pre-processed using the various image processing techniques such as sharpening, median filtering, and adaptive histogram equalization. The segmentation of the finger joints is carried out using the U-Net model, and the segmented binary image is converted to gray scale image using the subtraction method. The features are extracted using the Harris feature extractor, and classification of the proposed work is performed using Random Forest and Adaboost ML classifiers. The study included 50 RA patients and 50 normal subjects for the evaluation of RA. Data augmentation is performed to increase the number of images for U-Net segmentation technique. For the classification of RA and healthy subjects, the Random Forest classifier obtained an accuracy of 91.25% whereas the Adaboost classifier had an accuracy of 90%. Thus, the hybrid model using a Random Forest classifier can be used as an effective system for the diagnosis of RA.