Hyperspectral Imaging Combined With Deep Learning for Precision Grading of Clear Cell Renal Cell Carcinoma

Abstract

This study presents an integrated approach combining hyperspectral imaging (HSI) and deep learning for accurate grading of clear cell renal cell carcinoma (ccRCC). A refined preprocessing pipeline—including wavelet-based denoising and principal component analysis (PCA)—effectively enhances image quality and reduces data dimensionality. The proposed architecture utilizes a 1D convolutional neural network with attention mechanisms and a Transformer module to extract both local spectral features and global contextual information. Evaluated on a dataset of 80 ccRCC samples, the model achieves 90.32% accuracy, 89.65% sensitivity, and 90.15% specificity, outperforming several state-of-the-art models. These findings demonstrate the potential of HSI-based deep learning systems to improve diagnostic accuracy and support more precise, personalized treatment planning in renal oncology.