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Dementia01:30

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Quantum AI for psychiatric diagnosis: enhancing dementia classification with quantum machine learning.

Javaria Amin1, Muhammad Umair Ali2, Muhammad Zubair Islam2

  • 1Department of Computer Science, Rawalpindi Women University, Rawalpindi, Pakistan.

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Summary

A novel hybrid quantum-classical neural network (QCNN) combined with knowledge distillation significantly improves dementia classification accuracy using MRI scans. This approach enhances early detection and patient management through advanced quantum machine learning techniques.

Keywords:
classificationdeep learningdementiafeaturesquantum machine learning

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Area of Science:

  • Quantum Machine Learning (QML)
  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare

Background:

  • Early dementia detection is crucial for effective patient management, necessitating highly accurate classification methods.
  • Deep learning (DL) models handle large datasets, while quantum machine learning (QML) offers enhanced computational speed and data storage using qubits.
  • QML leverages quantum computing principles to potentially improve machine learning model efficiency and accuracy, especially for complex pattern recognition in imaging.

Purpose of the Study:

  • To propose and evaluate a hybrid quantum-classical convolutional neural network (QCNN) for accurate dementia classification using MRI data.
  • To investigate the efficacy of integrating quantum feature extraction with classical deep learning and knowledge distillation (KD) for improved performance.
  • To develop a scalable and efficient framework for dementia classification applicable in clinical settings.

Main Methods:

  • A hybrid QCNN framework was developed, processing MRI images through pre-processing, region of interest (ROI) extraction, and quantum feature mapping.
  • Pixel values from image patches were encoded as qubits and processed by a parameterized quantum circuit (PQC) to generate quantum features.
  • A knowledge distillation (KD) framework was employed, using a deeper CNN (teacher) to guide the QCNN (student) for enhanced generalization and feature learning.

Main Results:

  • The QCNN without KD achieved high accuracies: 0.9523 (ADNI-1), 0.9611 (ADNI-2), and 0.9412 (OASIS-2).
  • With KD, the student QCNN model demonstrated improved sensitivity, reaching an accuracy of up to 0.9978, surpassing existing state-of-the-art methods.
  • The hybrid approach showed superior performance compared to traditional ML/DL methods for dementia classification.

Conclusions:

  • The proposed hybrid quantum-classical CNN framework offers a highly accurate and efficient method for dementia classification from MRI data.
  • The integration of quantum feature extraction and knowledge distillation significantly enhances classification performance and model generalization.
  • This QML-based approach shows great promise for advancing early dementia detection and improving clinical patient management.