Positive-unlabeled learning (PU learning) only uses unlabeled samples and positive samples to train a binary classifier, while generative adversarial networks (GANs) obtain an image generator through adversarial training. In order to transfer the adversarial training method of GANs to PU learning for higher PU learning performance, the generator in GANs can be replaced with a classifier C, which selects samples in the unlabeled dataset to deceive the discriminator D and optimize C and D iteratively. This study proposes the JS-PAN model, which uses the Jensen-Shannon divergence (JS-divergence) as the objective function. Finally, according to the characteristics of data distribution and current needs, the rationality and high performance of the PAN model applied in the binary classification of medical diagnostic images are explained. Experiments on MNIST and CIFAR-10 datasets show that the KL-PAN model has higher accuracy (ACC) and F1-score than the similar PU learning models, and the JS-PAN model has higher performance in terms of two indicators after symmetric improvement, so the JS-PAN model is more reasonable. Experiments on three image subdatasets of Med-MNIST show that the KL-PAN model has almost the same ACC as the four benchmark supervised models, and JS-PAN has higher performance. Therefore, in view of both the excellent classification performance of the PAN model and the distribution characteristics of medical diagnostic data, PAN, as a semi-supervised learning method, can achieve faster and better results and thus show higher performance in the task of binary classification of medical images.