A Hybrid Deep Learning Framework for Liver Cancer Detection using Capsule Networks, Vision Transformers, Graph Neural Networks, and Quantum-Inspired Optimization

Liver cancer is one of the major causes of death in most parts of the world, this is mainly caused by late-stage diagnosis and complicated morphology of tumor. In this paper, shows a hybrid framework based on Python-based framework is suggested with combining Capsule Networks (CapsNet), Graph Neural Networks (GNN), Vision Transformer (ViT), and a Quantum-inspired Evolutionary Algorithm (QEA) to detect liver cancer early and accurately. Actually, prepared medical imaging data such as CT and MRI images are processed by adaptive histogram equalization and noise reduction to improve quality. CapsNet preserves hierarchical spatial variants and ViT learns long range relationships, GNN depicts structural relationships in visuals in the form of graphs. QEA identifies the best feature sets, enhancing the accuracy of classification and decreasing the computation costs. The results of the experiments have been shown to be highly robust and scaled, with better performance than traditional CNN-based techniques. The method has great potential of real-time clinical decision support, augmenting the earlier diagnosis and tailor-made treatment plans that will be applied in the liver cancer treatment.