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Automatic classification of pancreatic cancer from urinary biomarkers using equivariant quantum convolutional neural
Vivekanandan G1, Soma Prathibha2, Suganthi P1
1Assistant Professor, Department of Computer Science and Engineering, Sri Sairam Institute of Technology, Chennai, India.
None:
Early detection of Pancreatic Ductal Adenocarcinoma (PDAC) is crucial to improve survival rates. This study proposes an automated classification framework using equivariant quantum convolutional neural networks (EQCNNs) optimized with a Hybrid Adam-Dingo and Quantum Artificial Hummingbird Algorithm (HybADOQAHA). Urinary biomarkers - creatinine, lymphatic vessel endothelial hyaluronan receptor 1 (LYVE1), regenerating islet-derived protein 1 beta (REG1B), and trefoil factor 1(TFF1) were analyzed from 590 samples comprising healthy, benign, and PDAC cases. Pre-processing with dual-feature filtering and feature extraction via lifted Euler characteristic transform enhanced data quality. The experimental results demonstrate better accuracy, precision, recall, specificity, and Area Under the Curve (AUC) compared with baseline models, establishing the proposed method as a promising non-invasive diagnostic tool for early PDAC detection.
