Deep learning-assisted 3D model for the detection and classification of knee arthritis

Abstract

Osteoarthritis (OA) affects nearly 240 million people worldwide. It is a common degenerative illness that typically affects the knee joint OA causes pain, and functional disability, especially in older adults is a common disease. One of the most common and challenging medical conditions to deal with in old-aged people is the occurrence of knee osteoarthritis (KOA). Manual diagnosis involves observing X-ray images of the knee area and classifying it into different five grades. This requires the physician's expertise, suitable experience, and a lot of time, and even after that, the diagnosis can be prone to errors. Therefore, researchers in the machine learning (ML) and deep learning (DL) domains have employed the capabilities of deep neural network (DNN) models to identify and classify medical images in an automated, faster, and more accurate manner. Combining multiple imaging modalities or utilizing three-dimensional reconstructions can enhance the accuracy and completeness of 2D Images in diagnostic information. Hence to overcome the drawbacks of 2D imaging, the reconstruction of 3D models using 2D images is the main theme of our research. In this paper, we propose a deep learning-based model for the detection and classification of the early diagnosis of arthritis. It is a four-step procedure starting with data collection followed by data conversion. In this step, our proposed model deforms the target's convex hull to produce a 3D model. Herein, a series of 2D photos is utilized, along with surface rendering methods, to create a 3D model. In the third step, the feature extraction is performed followed by mesh refinement. The chamfer loss is optimized based on the rotational shape of the leg bones, and subsequently, the weight of the loss function can be allocated to the target's geometric properties. We have used a modified Gray Level Co-occurrence Matrix (GLCM) for feature extraction. In the fourth step, the image classification is performed and the suggested optimization strategy raises the model's accuracy. A comparison of results with current 3D reconstruction techniques proves that the suggested method can consistently produce a waterproof model with a greater reconstruction accuracy. The deep-seated intricacies and distinct patterns across arthritic phases are estimated through the extraction of complicated statistical variables combined with power spectral density. The high-dimensional data is divided into separate, easily observable groups using the Lion Optimization Algorithm and proposed distance metric. The F1 Score and Jaccard Metric showed an average of 0.85 and 0.23, indicating effective differentiation across clusters.