The aim of this paper is to examine the application of deep neural networks in analyzing cardiac images of transplanted heart biopsies. The goal was to develop a model that can classify the degree of tissue rejection of the transplant into categories 0R, 1R, 2R and 3R using DenseNet and ResNet, which are two modern architectures for deep neural networks. The introductory section provides a review of artificial neural network basics. In explaining why artificial neural networks were used, there is an illustration as to how they function and their differences in various implementations. The second part describes how image analysis was done using the developed models of artificial neural networks. Experiment results indicated both DenseNet and ResNet achieved satisfying performance, with a slight preference in favor of the DenseNet model when it comes to formaline-prepared pictures, while ResNet had better generalization capabilities on dehydrated images. By thoroughly analyzing the models’ performance, this research reveals some strengths and weaknesses inherent in the architectures employed here, as well as possible directions for further studies aimed at model optimization for medical imaging analysis. This work contributes to the understanding of the application of deep neural networks in medical diagnostics, especially in the context of heart transplants, and offers valuable insights for the development of automated clinical decision support systems.