A comparative analysis of deep learning models for disease classification in multi-organ histopathological images

Histopathological whole slide images (WSIs) provide critical information for disease diagnosis, yet their interpretation remains a time-consuming and expertise-dependent process. Recent advances in deep learning have shown promise in automating and improving histopathological analysis; however, the performance of convolutional neural networks (CNNs) and Vision Transformers (ViTs) across multi-organ disease classification remains insufficiently explored. In this study, we applied CNN- and ViT-based classification models to WSIs of major human organs, including the heart, lung, liver, and pancreas. Both single-organ models (classifying normal versus diseased tissue per organ) and integrated multi-organ models (classifying across organs within a unified framework) were evaluated. Model performance was compared in terms of classification accuracy. Among the ViT-based models, Swin-T achieved the highest accuracy (0.9964 ± 0.0036), while DenseNet-161 outperformed other CNN-based models (0.9811 ± 0.0078). Importantly, no substantial drop in classification performance was observed when extending from single-organ to multi-organ disease classification. These findings demonstrate that deep learning classification models based on ViTs and CNNs, particularly Swin-T and DenseNet-161, achieve robust performance in multi-organ histopathological disease classification. The results highlight their potential utility for the development of generalized diagnostic support software for multi-organ disease classification in histopathology and their future integration into routine digital pathology practice.