Quantum-Powered Vision Network for Intelligent Brain Analysis

Abstract

Brain tumor detection from magnetic resonance imaging (MRI) plays a vital role in early diagnosis and effective treatment planning. However, conventional manual analysis and classical deep learning models often face challenges related to high computational complexity, long training time, and limited scalability when handling high-dimensional medical image data. This project presents a Quantum-Powered Vision Network for Intelligent Brain Analysis, which integrates classical image pre-processing with quantum-assisted machine learning techniques to improve classification performance.

The proposed system follows a hybrid quantum–classical workflow that includes MRI image preprocessing, quantum data encoding using unary amplitude encoding, and quantum-assisted learning layers for feature extraction and classification. The model is designed to classify brain MRI images and identify tumor presence and levels efficiently. A structured methodology is implemented, and the working of the system is demonstrated through step-by-step output results.

Experimental observations show that the proposed approach achieves reliable classification behavior while maintaining com-putational efficiency compared to purely classical methods. The results highlight the feasibility and potential of quantum-assisted learning techniques in medical image analysis. This project demonstrates that hybrid quantum-classical models can serve as a promising direction for future intelligent healthcare and diagnostic systems.