One of the most prevalent cancers in the world is kidney cancer (KC). A precise diagnosis, which is influenced by a number of variables, such as the size or volume of the tumor, the types and stages of the cancer, etc., is essential for the treatment of patients with kidney cancer. In this work two main types of kidney cancer: normal and abnormal, using the accessible KiTS21 dataset of contrast-enhanced CT scans and associated data from patients. Many of these techniques show poor accuracy, which raises doubts regarding their efficiency and dependability. To overcome these limitations, this paper presents the use of a double-decker convolutional neural network with the great wall construction algorithm (DDCNN-GWCA). Hybrid quick conventional bilateral filter improves the quality of pre-processed data by reducing noise while preserving crucial information by using the KiTS21 dataset. Practical Quantum K-Means Clustering is used for segmentation to improve detection efficiency and accuracy. The Q-value Regularized Transformer (QT) is a feature extraction method that combines the power of transformers with Q-value regularization to capture the relevant features. A Double-Decker Convolutional Neural Network's multi-layered architecture is used for classification to identify the classes. The Great Wall Construction Algorithm is an innovative optimization technique that optimizes the hyperparameters of the Double Decker Convolutional Neural Network (DDCNN), ensuring enhanced performance. It obtained scores of 98.9% for the KiTS21 dataset. These results demonstrate the strategy's ability to outperform existing methods and open the way for major advances in the diagnosis of kidney cancer.