2025/12/10 by Sivaselvi Gunasekaran · 1 voice
Medicine · Neuroscience · #Brain Tumor Detection and Classification #Lung Cancer Research Studies #Radiomics and Machine Learning in Medical Imaging
paper · pdf · doi:10.22214/ijraset.2025.76116
openalex created_date 2025/12/10 · openalex publication_date 2025/12/10 · openalex updated_date 2026/05/21
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.