Decellularized tissues are used as transplant materials and scaffolding in regenerative medicine. Histological evaluation is used to assess decellularization and reveal residual cell nuclei and changes in the structure of the extracellular matrix. However, qualitative evaluation depends on the subjectivity of the evaluator. Therefore, in this study, an AI-based image classification method for objective evaluation of histological decellularization was developed and used to evaluate decellularization in stained images. Two image classifications were performed: untreated aorta and high hydrostatic pressure (HHP)-decellularized aorta, and untreated aorta and sodium dodecyl sulfate (SDS)-decellularized aorta. Both sets of images were classified with high accuracy. Accuracy, precision, recall [true positive rate (TPR)], false positive rate (FPR), F1-score, and area under the receiver operating characteristics curve (AUC-ROC) of the two classifications indicated that the AI-based classification method developed in this study accurately assessed decellularization. However, the TPR revealed that untreated aortas had a higher probability of being misidentified as HHP-decellularized aortas than as SDS-decellularized aortas. One factor that may have contributed to the misidentification of images of untreated aortas as those of decellularized aortas was that feature weighting was performed on other features other than the presence or absence of cell nuclei. Heatmaps were generated based on the results of image classification of stained images of decellularized aortas prepared by the two decellularization techniques. Therefore, the uniformity of decellularization could be visualized. The method developed in this study allows quantification of decellularization heterogeneity within decellularized tissues, which was previously unquantifiable. This method can be adapted to a wide variety of decellularized tissues and may contribute to rapid and efficient identification of decellularized tissues.