Osteoarthritis Detection Using Deep Learning-Based Semantic GWO Threshold Segmentation

Abstract

Knee osteoarthritis (OA) has been a prevalent degenerative joint ailment that affects people all over the world. Because of the increased occurrence of knee OA, the accurate diagnosis of osteoarthritis at an early stage is a tough task. The osteoarthritis imaging such as conventional radiography, MRI, and ultrasound are the essential components to diagnose knee OA in its early stages. On the other hand, deep neural network (DNN) designs are extensively used in medical image examination for the accurate outcomes in terms of classification of OA diagnosis. Image segmentation, also known as pixel-level categorization, is the method of categorizing portions of an image that are composed of the exact same object class by means of partitioning images into multiple segments. But, the drawback identified from the lack of accuracy of the traditional approach can be overcome using deep learning method. Hence, this paper presents a deep learning-based semantic grey wolf optimization (GWO) threshold segmentation to detect the Osteoarthritis accurately at all stages. The two phases of stages involve in the proposed work which carries CT image normalization and histogram connection to enhance the image with accuracy. The comparative analysis has also been done with the existing methods using the evaluation parameters such as sensitivity, specificity, accuracy, MSE, PSNR, SSIM, and MAE for the accurate diagnosis of OA.