MambaOutと潜在拡散モデルを用いたX線膝関節症分類の高度化：マルチ分類アプローチ

Knee Osteoarthritis (KOA) is a prevalent degenerative joint disease affecting millions worldwide. Accurate classification of KOA severity is crucial for effective diagnosis and treatment planning. This study introduces a novel multiclassification algorithm for X-ray KOA based on MambaOut and Latent Diffusion Model (LDM). MambaOut, an emerging network architecture, achieves superior classification performance compared to fine-tuning the mainstream Convolutional Neural Networks (CNNs) for KOA classification. To address sample imbalance across KL grades, we propose an AI-generated model using LDM to synthesize new data. This approach enhances minority-class samples by optimizing the autoencoder's loss function and incorporating pathological labels into the LDM framework. Our approach achieves an average accuracy of 86.3%, an average precision of 85.3%, an F1 score of 0.855, and a mean absolute error reduced to 14.7% in the four-classification task, outperforming recent advanced methods. This study not only advances KOA classification techniques but also highlights the potential of integrating advanced neural architectures with generative models for medical image analysis.