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Convolutional neural networks (CNN) have a strong feature extraction ability for images and present a high level of efficiency and accuracy in object detection and image recognition. When CNN is used to model microwave devices, the existing literature generally uses its size parameters as one-dimensional (1-D) input, which does not give full play to the image-processing ability of CNN. In order to make full use of the characteristics of CNN, this letter converts the 1-D input of microwave devices into the form of an image model, that is, the 1-D input is transformed into a two-dimensional (2-D) matrix composed of 0 and 1 as the input. The image model is combined with CNN, called image-based CNN (ICNN), which establishes a deep learning surrogate model between the physical parameters and electrical properties of microwave devices and improves the accuracy and generalization ability of the model. Taking the resonant frequency of the microstrip antenna as a simulation example, modelling was carried out by the proposed ICNN and compared with the mainstream machine learning methods. The results show that the proposed method has high convergence and fitting accuracy. [ABSTRACT FROM AUTHOR]