Clear cell renal cell carcinoma (ccRCC) is the most common form of renal cell carcinoma in adults, comprising approximately 80% of cases. The lethality of ccRCC rises significantly at stage III or beyond, emphasizing the need for early detection to enable timely therapeutic interventions. This study introduces a non-invasive and efficient classification method, Domain Adaptive Squeeze-and-Excitation Network (DASNet), for grading ccRCC through Computed Tomography (CT) images using advanced deep learning and machine learning techniques. The dataset is enhanced using MedAugment technology and balanced to improve generalization and classification performance. To mitigate overfitting, renal angiomyolipoma (AML) samples are incorporated, increasing data diversity and model robustness. EfficientNet and RegNet serve as foundational models, leveraging local feature extraction and Squeeze-and-Excitation (SE) attention mechanisms to enhance recognition accuracy across grades. Furthermore, Domain-Adversarial Neural Networks (DANNs) are employed to maintain consistency between source and target domains, bolstering the model's generalization ability. The proposed model achieves a classification accuracy of 97.50%, demonstrating efficacy in early ccRCC grade identification. These findings not only offer valuable clinical insights but also establish a foundation for broader application of deep learning in tumor detection.

Keywords: Domain-adversarial neural network; Fusion model; Local feature extraction; Renal cancer prediction; SE attention mechanism.

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