Automated Detection of Alzheimer’s via Hybrid Classical Quantum Neural Networks

Electronics

Haroon Zafar¹  Ahmad Almogren²  Tayyaba Shahwar³  Ateeq Ur Rehman³  Junaid Zafar³  Muhammad Shafiq⁴  Habib Hamam⁵ 

[1] Cardiovascular Research & Innovation Centre Ireland, School of Medicine, National University of Ireland Galway, H91 TX33 Galway, Ireland;Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia;Department of Electrical Engineering, Government College University, Lahore 54000, Pakistan;Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea;Faculty of Engineering, Uni de Moncton, Moncton, NB E1A3E9, Canada;

关键词: machine learning;    deep neural network;    quantum computing;    quantum machine learning;    quantum neural network;    Alzheimer’s disease;   

DOI  :  10.3390/electronics11050721

来源: DOAJ

【 摘 要 】

Deep Neural Networks have offered numerous innovative solutions to brain-related diseases including Alzheimer’s. However, there are still a few standpoints in terms of diagnosis and planning that can be transformed via quantum Machine Learning (QML). In this study, we present a hybrid classical–quantum machine learning model for the detection of Alzheimer’s using 6400 labeled MRI scans with two classes. Hybrid classical–quantum transfer learning is used, which makes it possible to optimally pre-process complex and high-dimensional data. Classical neural networks extract high-dimensional features and embed informative feature vectors into a quantum processor. We use resnet34 to extract features from the image and feed a 512-feature vector to our quantum variational circuit (QVC) to generate a four-feature vector for precise decision boundaries. Adam optimizer is used to exploit the adaptive learning rate corresponding to each parameter based on first- and second-order gradients. Furthermore, to validate the model, different quantum simulators (PennyLane, qiskit.aer and qiskit.basicaer) are used for the detection of the demented and non-demented images. The learning rate is set to 10⁻⁴ for and optimized quantum depth of six layers, resulting in a training accuracy of 99.1% and a classification accuracy of 97.2% for 20 epochs. The hybrid classical–quantum network significantly outperformed the classical network, as the classification accuracy achieved by the classical transfer learning model was 92%. Thus, a hybrid transfer-learning model is used for binary detection, in which a quantum circuit improves the performance of a pre-trained ResNet34 architecture. Therefore, this work offers a method for selecting an optimal approach for detecting Alzheimer’s disease. The proposed model not only allows for the automated detection of Alzheimer’s but would also speed up the process significantly in clinical settings.

【 授权许可】

Unknown