Ordinal Regression Research Based on Dual Loss Function-An Example on Lumbar Vertebra Classification in CT Images

Background/Objectives: Some classification problems involve ordered categories (e.g., low-medium-high), which are better modeled as ordinal regression. This study aimed to propose and evaluate a dual loss framework-Ordinal Residual Dual Loss-for lumbar vertebra classification on CT images to assist L3 identification and sarcopenia detection. Methods: In this retrospective study, lumbar spine CT images were used to train a deep learning model based on a MobileNet-v3-Large network. The proposed framework combines standard cross-entropy loss for classification with an Ordinal Residual Loss defined on the difference between output probabilities and target ordinal probabilities. Results: Experimental results show that the Ordinal Residual Dual Loss approach outperforms using cross-entropy alone and also surpasses methods from previous studies in lumbar vertebra classification performance. Conclusions: Leveraging a dual loss design that incorporates ordinal information improves vertebral level classification on CT images and has potential to support more accurate automated L3 localization and sarcopenia assessment in clinical practice.