Research on Prostate Cancer Pathological Image Classification Method Based on Vision Transformer

Prostate cancer is a significant health concern, and accurate diagnosis is crucial for effective patient management. Pathological image analysis plays a vital role in diagnosing prostate cancer by providing insights into tissue characteristics. In this study, we investigate the application of Vision Transformer (ViT) technology for classifying pathological images of prostate cancer. Our proposed methodology includes various data preprocessing techniques such as cropping and grayscale normalization. Additionally, we employ data augmentation and upsampling operations to address dataset imbalances. The ViT model is utilized for model construction and training, where normalized pathological images are used for parameter training. During the testing stage, a batch of new prostate cancer pathological image data is used to evaluate and assess the performance of the trained model. The experimental results show that the classification accuracy is 0.767, which surpasses of other classical classification models. This study highlights ViT technology utility in the field of pathological image analysis. The promising results pave the way for improved accuracy and efficiency in clinical applications, thereby enhancing the diagnosis and treatment of prostate cancer.