Quantum AI Based Diagnosis System for Early Brain Tumor and Lung Cancer Detection
Abstract
Early and correct diagnosis plays a key role in enhancing the outcome in brain tumor and lung cancer patients.
Traditional Artificial Intelligence (AI) and Deep Learning (DL) models are handicapped by expensive computational costs and
challenges in handling high-dimensional medical data. This research article presents a Quantum AI based Diagnostics System
employing a hybrid quantum-classical computing model. The framework combines traditional feature preprocessing with the
exponential power of quantum computing. For classification in brain tumors (e.g., LGGs/HGGs), the quantum core uses a
Hybrid Quantum-Classical Integrated Neural Network (HQCINN) or Variational Quantum Classifier (VQC). For lung cancer
prediction, the framework might employ quantum-enhanced clustering such as Quantum-Enhanced K-Medoids or optimization
models such as Quantum --Genetic Binary Grey Wolf Optimizer (Q-GBGWO) with Extreme Learning Machines (ELM). This
hybrid methodology is intended to achieve superior diagnostic efficacy and speed across both MRI and CT modalities, laying the
groundwork for faster and more individualized clinical diagnostics.
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