When doctors use contrast-enhanced computed tomography (CECT) images to predict the metastasis of axillary lymph nodes (ALN) for breast cancer patients, the prediction performance could be degraded by subjective factors such as experience, psychological factors, and degree of fatigue. This study aims to exploit efficient deep learning schemes to predict the metastasis of ALN automatically via CECT images. A new construction called deformable sampling module (DSM) was meticulously designed as a plug-and-play sampling module in the proposed deformable attention VGG19 (DA-VGG19). A dataset of 800 samples labeled from 800 CECT images of 401 breast cancer patients retrospectively enrolled in the last three years was adopted to train, validate, and test the deep convolutional neural network models. By comparing the accuracy, positive predictive value, negative predictive value, sensitivity and specificity indices, the performance of the proposed model is analyzed in detail. The best-performing DA-VGG19 model achieved an accuracy of 0.9088, which is higher than that of other classification neural networks. As such, the proposed intelligent diagnosis algorithm can provide doctors with daily diagnostic assistance and advice and reduce the workload of doctors. The source code mentioned in this article will be released later.