QUANTUM CONVOLUTIONAL NEURAL NETWORK FOR PANCREATIC CANCER IMAGE CLASSIFICATION

Pancreatic cancer is among the most lethal malignancies, also late-stage diagnosis causes poor prognosis and significant fatality rates.Early identification of computed tomography (CT) images may improve survival rates nevertheless, current approaches suffer from noise, artefacts, and complex data structures.Quantum convolutional neural networks (QCNNs) are a possible substitute since their application of quantum principles like superposition and entanglement helps increase the accuracy of feature extraction and categorisation.This review investigates the integration of QCNN with deep learning models, including ALOCNN-GRU and FOAbased SVM optimization for pancreatic cancer detection.The hierarchical convolution and pooling layers of QCNN make it easier and more reliable to find cancerous tissue in medical images.Moreover, advances in segmentation and early diagnosis entail the Richer Convolutional Feature (RCF) approach and the Normalised Information for Modular Divergence Quality (NESTED) method.The findings provide a more accurate and dependable pancreatic cancer detection as well as highlight how well QCNN could change medical picture classification.Future developments in multi-scale learning and optimisation strategies might greatly improve diagnosis accuracy and treatment planning.