Design of Deep Learning Framework for Precise Grading of Clear Cell Renal Cell Carcinoma Based on Convolutional Neural Networks

This study develops a sophisticated deep learning framework to enhance the classification and grading of clear cell renal cell carcinoma (ccRCC) using pathological slide images. By integrating Convolutional Neural Networks (CNNs) with Transformer modules, the model innovatively addresses the challenges presented by the subtle morphological variations within ccRCC tissue characteristics. Our dataset comprised pathological slides from 52 ccRCC patients, accurately classified under the International Society of Urological Pathology(ISUP) grading system. The model demonstrated superior performance metrics, achieving an accuracy of 85.8%, sensitivity of 87.9%, and specificity of 83.1% in distinguishing between various grades of ccRCC. These results highlight the potential of our model to substantially improve diagnostic precision and provide significant support in clinical decision-making.