Bladder cancer stage classification using LCANet segmentation and multi-stacked GAT with leopard seal optimization

One of the common types of cancer is that of the bladder, and it is important to detect this condition early for effective treatment, as well as ensuring survival rates are higher. In this paper, we suggest a comprehensive deep learning approach for automatic detection and classification of bladder cancer using T2-weighted Magnetic Resonance Imaging (MRI). This was done by initially processing the first set of bladder MRI, with resizing augmentations applied to equalize the sizes of the images, as well as adding variance to the samples. A Lightweight Context-Aware Network (LCANet) was later used to precisely trace the bladder and the tumor within the neural network. The tumor layer was classified with a Multi-Stacked Layered Graph Attention Network (MSL-GAT) because it is proficient at obtaining features and able to distinguish between non-muscle-invasive (NMIBC) and muscle-invasive (MIBC) bladder cancer. The Leopard Seal Optimization (LSO) algorithm was also utilized for further optimization of major parameters within the network to ensure its convergence and accuracy. The developed model achieved 98.06% AUC and proved to be accurate in its classification of the data from two clinical centers, showing low error rates during classifying. These results suggest that the suggested model is highly generalizable and applicable. The study shows that it may be possible to use the proposed approach to enable radiologists to early diagnose and manage the condition.