Pneumonia is a prevalent respiratory disease for which early diagnosis is crucial to effective treatment. This study proposes a hybrid model, CTFNet, which combines convolutional neural network (CNN) and Transformer to aid in the effective and accurate diagnosis of pneumonia. The model integrates a convolutional tokenizer and a focused linear attention mechanism. The convolutional tokenizer performs more compact feature extraction through convolution operations, retaining key local features of images while reducing computational complexity to enhance model expressiveness. The focused linear attention mechanism reduces the computational demands of the Transformer and optimizes the attention framework, significantly improving model performance. On the Chest X-ray Images dataset, CTFNet demonstrates outstanding performance in pneumonia classification tasks, achieving an accuracy of 99.32%, a precision of 99.55%, a recall of 99.55%, and an F1-score of 99.55%. The impressive performance highlights the model’s potential for clinical applications. The model is evaluated on the COVID-19 Radiography Database dataset for its generalization ability. In this dataset, CTFNet achieves an accuracy above 98% in multiple binary classification tasks. These results indicate that CTFNet exhibits strong generalization ability and reliability across various tasks in pneumonia image classification.