Implementing Magnetic Resonance Imaging Brain Disorder Classification via AlexNet–Quantum Learning

Abstract

The classical neural network has provided remarkable results to diagnose neurological disorders against neuroimaging data. However, in terms of efficient and accurate classification, some standpoints need to be improved by utilizing high-speed computing tools. By integrating quantum computing phenomena with deep neural network approaches, this study proposes an AlexNet–quantum transfer learning method to diagnose neurodegenerative diseases using magnetic resonance imaging (MRI) dataset. The hybrid model is constructed by extracting an informative feature vector from high-dimensional data using a classical pre-trained AlexNet model and further feeding this network to a quantum variational circuit (QVC). Quantum circuit leverages quantum computing phenomena, quantum bits, and different quantum gates such as Hadamard and CNOT gate for transformation. The classical pre-trained model extracts the 4096 features from the MRI dataset by using AlexNet architecture and gives this vector as input to the quantum circuit. QVC generates a 4-dimensional vector and to transform this vector into a 2-dimensional vector, a fully connected layer is connected at the end to perform the binary classification task for a brain disorder. Furthermore, the classical–quantum model employs the quantum depth of six layers on pennyLane quantum simulators, presenting the classification accuracy of 97% for Parkinson’s disease (PD) and 96% for Alzheimer’s disease (AD) for 25 epochs. Besides this, pre-trained classical neural models are implemented for the classification of disorder and then, we compare the performance of the classical transfer learning model and hybrid classical–quantum transfer learning model. This comparison shows that the AlexNet–quantum learning model achieves beneficial results for classifying PD and AD. So, this work leverages the high-speed computational power using deep network learning and quantum circuit learning to offer insight into the practical application of quantum computers that speed up the performance of the model on real-world data in the healthcare domain.

Multiclass seismic damage detection of buildings using quantum convolutional neural network

“…The development of quantum CNN (QCNN) algorithms has been motivated by the benefits of CNN, together with the potential power of QML (Hur et al., 2022; Kerenidis et al., 2019; Y. Li et al., 2020) in various fields. The adoption of QNN has increased in medical sectors to classify medical images as evidenced by this research work (Alsharabi et al., 2023; Z. Li et al., 2022; Houssein et al., 2022). The potential advantage of using hybrid QNN for drug response prediction is demonstrated by Sagingalieva et al.…”

“…Li et al, 2020) in various fields. The adoption of QNN has increased in medical sectors to classify medical images as evidenced by this research work (Alsharabi et al, 2023;Z. Li et al, 2022;Houssein et al, 2022).…”

Beyond Limits: Charting New Horizons in Glioma Tumor Classification through Hybrid Quantum Computing with The Cancer Genome Atlas (TCGA) Data

“…Furthermore, in the literature, it has been indicated in numerous studies that hybrid methods enhance the performance of quantum machine learning techniques. In certain studies, although the performance advantage of the proposed hybrid classicalquantum methods over classical methods is emphasized when applied to problems such as brain tumor classification [118], automatic Alzheimer's diagnosis [119], brain disorder classification [120], and diabetic foot ulcer classification [121], some research has highlighted the advantage of rapid convergence provided by hybrid models, leading to a shortened processing time, even when the performance is equivalent [122].…”

Hearing loss classification via AlexNet and Support Vector Machine

“…The input parameters of each layer of the model are shown in Figure 2. Among the convolutional layers, it is noteworthy that the third and fourth convolutional layers are no Maxpooling [21,22] the basic structure of the convolutional layer is Convolutional and ReLU, while the basic structure of the first, second, and fifth convolutional layers is Convolutional, ReLU, and Maxpooling. The full connection layers are named FC1, FC2, and FC3 respectively [23,24].…”