AI-Driven Transformer Model for Knee Arthritis Detection: Advancements in X-ray-Based Diagnosis and Severity Classification

Abstract

Knee arthritis functions as a degenerative joint condition which creates strong impact on joint movement and patient lifestyle quality. Medical professionals need early and precise arthritis severity assessment through X-ray imaging to facilitate appropriate medical actions and care design. CNNs and other basic deep learning frameworks have demonstrated successful performance in medical image evaluation yet experience challenges when identifying extensive interconnections and intricate configurations. The research explores transformer models as a solution for knee arthritis recognition and image-based severity assessment through X-ray scans. The proposed approach adopts Swin Transformer because it conducts hierarchical feature processing with global context preservation capabilities. Research prepared and tested their model by processing X-ray images from public databases through enhanced contrast along with augmented data normalization techniques. The research findings demonstrate transformer models achieve superior performance than convolutional neural networks by providing both higher total precision and adaptability to images and better detection accuracy when used for classification. Modern research has utilized AI transformers to enable better automated arthritis diagnosis capabilities thus developing new prospects in medical imaging.

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