Anchor-free-based detection methods have been proposed successively in recent years, and they transform objects into key points and assign labels to positive and negative samples in the global Gaussian heatmap. This label assignment strategy suffers from positive and negative sample imbalance in some scenarios and cannot effectively reflect the shape and orientation of the object in parathyroid detection. Therefore, a new parathyroid detection model, namely, EllipseNet, is proposed in this study, which first constructs an elliptical Gaussian distribution in GT to fit the real object in GT, so as to make the assignment of positive and negative samples more fine-grained. Furthermore, a loss function incorporating the object shape information is proposed to constrain the position of the object, so as to improve the accuracy of detection. In addition, multi-scale prediction is constructed in the model, which can better detect objects of different sizes and solve the problem of target scale imbalance in parathyroid detection. In this study, experiments are conducted on the parathyroid dataset, and the results show that EllipseNet achieves an AP₅₀ of 95%, which is a large improvement in detection accuracy compared with a variety of mainstream detection algorithms.