Osteoarthritis Detection and Classification in Knee X-Ray Images Using Particle Swarm Optimization with Deep Neural Network

Abstract

Explainable artificial intelligence (XAI) involves a collection of processes and approaches which enables human users to comprehend and trust the results and output produced by machine learning (ML) approaches. XAI is employed for describing the AI model, its expected impact, and potential biases. At the same time, Internet of Healthcare Things (IoHT) has become a hot research topic in the healthcare sector which assist in the disease diagnostic process. Presently, an efficient computer-aided diagnosis (CAD) model is needed for diagnosing osteoarthritis (OA). This study designs a new particle swarm optimization (PSO) model with deep neural network (DNN), named PSO-DNN technique, for the identification and categorization of osteoarthritis from the knee X-ray images in an IoHT environment. The presented method helps to distinguish between well and diseased knee X-ray images. Here, a guided filter (GF) and adaptive histogram equalization models are correspondingly employed to remove noises and enhance the images. Global thresholding-based segmentation model is employed for extracting the synovial cavity regions from the image, and curvature values are determined. For drawing a good validation, the experimentation takes place on the real-time patient-oriented images gathered from the medical organizations. From the simulation outcome, the presented PSO-DNN model confirmed the superior performance of the applied images.