Synergistic Integration of 3D CNN and Vision Transformers for Enhanced Bio-Medical for Knee Cartilage Pathology Detection

Degeneration of knee cartilage is a significant health concern, particularly among the elderly and individuals with a history of joint pain. Early diagnosis and classification are crucial for effective intervention and treatment. The proposed work aims to develop an AI-based diagnostic model that combines 3D convolutional neural networks (CNN) with 3D vision transformer (ViT) to extract high-level spatial information from 3D MRI images and improve the recognition of subtle patterns in cartilage degeneration and classify degeneration into stages: healthy cartilage, mild cartilage, severe cartilage, cartilage lesions, and osteoarthritis-related changes. 3D CNN and 3D ViT were used to extract spatial hierarchies and features from MRI data and trained on annotated knee MRI scans to improve classification. 3D CNN and 3D ViT models outperform the methods in the classification of knee cartilage degeneration, providing accurate and reliable disease detection for bio-medical purposes. The model achieved an accuracy of 90.46%. Combining 3D CNN with a 3D ViT effectively identifies cartilage degeneration in the knee. The technology helps increase diagnostic accuracy, shorten analysis time, and create personalized treatment plans. This strategy can improve patient outcomes through timely intervention and is particularly useful for early diagnosis and treatment of degenerative diseases.