Quantum AI Based Diagnosis System for Early Brain Tumor and Lung Cancer Detection

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.