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Magnetic resonance imaging (MRI) is a common technique to scan brains forstrokes, tumors, and other abnormalities that cause forms of dementia. However,correctly diagnosing forms of dementia from MRIs is difficult, as nearly 1 in 3patients with Alzheimer's were misdiagnosed in 2019, an issue neural networkscan rectify. Quantum computing applications This proposed novel neural networkarchitecture uses a fully-connected (FC) layer, which reduces the number offeatures to obtain an expectation value by implementing a variational quantumcircuit (VQC). The VQC created in this study utilizes a layer of Hadamardgates, Rotation-Y gates that are parameterized by tanh(intensity) * (pi/2) of apixel, controlled-not (CNOT) gates, and measurement operators to obtain theexpected values. This study found that the proposed hybrid quantum-classicalconvolutional neural network (QCCNN) provided 97.5% and 95.1% testing andvalidation accuracies, respectively, which was considerably higher than theclassical neural network (CNN) testing and validation accuracies of 91.5% and89.2%. Additionally, using a testing set of 100 normal and 100 dementia MRIimages, the QCCNN detected normal and demented images correctly 95% and 98% ofthe time, compared to the CNN accuracies of 89% and 91%. With hospitals likeMassachusetts General Hospital beginning to adopt machine learning applicationsfor biomedical image detection, this proposed architecture would approveaccuracies and potentially save more lives. Furthermore, the proposedarchitecture is generally flexible, and can be used for transfer-learningtasks, saving time and resources.

Implementing a Hybrid Quantum-Classical Neural Network by Utilizing a Variational Quantum Circuit for Detection of Dementia