A Random Graph Diffusion Attention Network with Great Wall Construction and Clinical Metadata for Improved Kidney Cancer Diagnosis and Surgical Planning

Kidney cancer develops through abnormal cell multiplication within the renal cortex or pelvis and often results from smoking combined with obesity and hypertension as well as genetic predisposition. The condition produces three main signs: hematuria, flank pain and weight loss. Current deep learning detection algorithms show low accuracy and high error rates. The Random Graph Diffusion Attention Network with Great Wall Construction (RGDAN-GWCA) method aims to enhance detection accuracy and optimize classification performance for solving this issue. This study uses RGDAN-GWCA to detect and Surgical Planning the kidney cancer. This proposed approach integrates CT images with clinical data for its operation. The proposed method adopts KiTS21 because it represents a standalone dataset. The proposed method solves major diagnostic obstacles for kidney cancer through advanced image preprocessing methods and extraction techniques and classification algorithms. The analysis of CT images occurred with Gradient Domain Guided Filtering (GDGF) and the Spike-driven transformer method implemented metadata refinements. The method Inverse Z-transform and Wiener Hopf Factorization (InZ-Tr-WHF) for CT image feature extraction with Min Max Normalization processing of clinical metadata. The RGDAN-GWCA system used a Great Wall Construction Algorithm to combine and analyze extracted features for performing accurate patient classification. This proposed methodology delivers outstanding results for all performance measurement criteria which include 99.65% accuracy along with 99.61% precision, 99.63% recall, 99.56% specificity and 99.62% F1-score. Clinical assessment of tumor volume alongside cancer stage established themselves as the key medical indicators used by surgeons for making treatment decisions. The surgical planning method shows remarkable promise to assist doctors in deciding appropriate nephrectomy treatments for patients dealing with kidney cancer.