  Magnetic resonance imaging (MRI) is a common technique to scan brains for
strokes, tumors, and other abnormalities that cause forms of dementia. However,
correctly diagnosing forms of dementia from MRIs is difficult, as nearly 1 in 3
patients with Alzheimer's were misdiagnosed in 2019, an issue neural networks
can rectify. Quantum computing applications This proposed novel neural network
architecture uses a fully-connected (FC) layer, which reduces the number of
features to obtain an expectation value by implementing a variational quantum
circuit (VQC). The VQC created in this study utilizes a layer of Hadamard
gates, Rotation-Y gates that are parameterized by tanh(intensity) * (pi/2) of a
pixel, controlled-not (CNOT) gates, and measurement operators to obtain the
expected values. This study found that the proposed hybrid quantum-classical
convolutional neural network (QCCNN) provided 97.5% and 95.1% testing and
validation accuracies, respectively, which was considerably higher than the
classical neural network (CNN) testing and validation accuracies of 91.5% and
89.2%. Additionally, using a testing set of 100 normal and 100 dementia MRI
images, the QCCNN detected normal and demented images correctly 95% and 98% of
the time, compared to the CNN accuracies of 89% and 91%. With hospitals like
Massachusetts General Hospital beginning to adopt machine learning applications
for biomedical image detection, this proposed architecture would approve
accuracies and potentially save more lives. Furthermore, the proposed
architecture is generally flexible, and can be used for transfer-learning
tasks, saving time and resources.
