Deep learning is widely used in medical image analysis, but obtaining large-scale annotation data is challenging due to the need for expert medical professionals to perform the annotations. This paper proposes a semi-supervised active learning framework to classify medical images with limited annotated data, helping clinicians assess lung tumor risk levels. This study designs an active learning strategy that estimates the uncertainty of samples based on their training dynamics, allowing for the selection of the most informative samples for manual labeling. To address the cold start problem of active learning, the framework constructs an initial labeled dataset with a uniform distribution using unsupervised training and clustering techniques. The semi-supervised learning framework strategically combines limited annotated samples with abundant unlabeled datasets during model training. This study conducts supervised training on the labeled data, while consistency regularization and pseudo-labeling techniques are applied to the unlabeled data. The framework effectively combines the advantages of active learning and semi-supervised learning. Compared to traditional semi-supervised learning frameworks, this framework effectively prioritizes the selection of samples on the decision boundary for labeling. Moreover, in each iteration of the active learning training process, the framework integrates the latest semi-supervised trained model, further improving the selection performance of active learning. Extensive evaluations on a clinical lung tumor surgical lesion slice image dataset show that the proposed framework achieves 88.79% accuracy, 88.74% precision, and an F1-score of 0.9147, surpassing existing baseline methods.