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Enhanced brain tumour prediction using quantum: a hybrid deep learning approach.

K Valarmathi¹, N Aniruddhan², S Adithya Vardhan²

¹School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India. valarmathi.k@vit.ac.in.

Scientific Reports

|June 25, 2026

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This study introduces a hybrid deep learning and quantum transfer learning model for brain tumor diagnosis from MRI scans. The novel approach significantly improves diagnostic accuracy to 95%, outperforming traditional methods.

Area of Science:

Medical Imaging
Quantum Computing
Artificial Intelligence

Background:

Brain tumors require timely and accurate diagnosis for effective treatment.
Deep learning models like ResNet show promise but face challenges with computational complexity and high-dimensional data.
Existing methods struggle with scalability and efficiency in processing complex medical imaging data.

Purpose of the Study:

To develop a hybrid quantum-classical model for enhanced brain tumor diagnosis.
To leverage quantum transfer learning to overcome limitations of traditional deep learning in medical imaging.
To improve diagnostic accuracy and computational efficiency in analyzing MRI scans.

Main Methods:

A hybrid approach combining ResNet for feature extraction and quantum circuits for classification was developed.

Keywords:

Brain tumour detection Convolutional neural network Deep learning Hybrid quantum computing MRI image analysis PennyLane Quantum computing Quantum machine learning ResNet Support vector machine

Related Experiment Videos

Quantum transfer learning was employed using the PennyLane framework for hybrid quantum-classical integration.

The model was trained and evaluated on MRI scans for brain tumor detection.

Main Results:

The hybrid model achieved 95% diagnostic accuracy, a significant improvement over traditional CNN models (82%).
The proposed method demonstrated superior performance and generalization compared to conventional deep learning approaches.
The model minimized computational overhead while maximizing diagnostic accuracy.

Conclusions:

Quantum computing offers a transformative potential for radiology and medical imaging.
Hybrid quantum-classical models represent a significant advancement in medical diagnostics.
This approach paves the way for faster, more precise, and scalable diagnostic tools, improving patient outcomes.