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

Shahwar, Tayyaba; Zafar, Junaid; Almogren, Ahmad; Zafar, Haroon; Rehman, Ateeq Ur; Shafiq, Muhammad; Hamam, Habib

doi:10.3390/electronics11050721

Cited by 40 publications

(23 citation statements)

References 41 publications

(35 reference statements)

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“…The results of the DEMNET model achieved a testing accuracy of 95.23%. Similarly, [21] applied a hybrid classical quantum NN to a Kaggle dataset containing 6400 MRI images for automated detection of AD, and ResnNet34 was used for feature extraction. The proposed hybrid classical quantum network achieved the highest testing accuracy of 97.2%.…”

Section: Previous Workmentioning

confidence: 99%

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A Deep Learning for Alzheimer’s Stages Detection Using Brain Images

Ullah¹,

Jamjoom²

2023

Computers, Materials &Amp; Continua

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Alzheimer's disease (AD) is a chronic and common form of dementia that mainly affects elderly individuals. The disease is dangerous because it causes damage to brain cells and tissues before the symptoms appear, and there is no medicinal or surgical treatment available yet for AD. AD causes loss of memory and functionality control in multiple degrees according to AD's progression level. However, early diagnosis of AD can hinder its progression. Brain imaging tools such as magnetic resonance imaging (MRI), computed tomography (CT) scans, positron emission tomography (PET), etc. can help in medical diagnosis of AD. Recently, computer-aided diagnosis (CAD) such as deep learning applied to brain images obtained with these tools, has been an established strategic methodology that is widely used for clinical assistance in prognosis of AD. In this study, we proposed an intelligent methodology for building a convolutional neural network (CNN) from scratch to detect AD stages from the brain MRI images dataset and to improve patient care. It is worth mentioning that training a deep-learning model requires a large amount of data to produce accurate results and prevent the model from overfitting problems. Therefore, for better understanding of classifiers and to overcome the model overfitting problem, we applied data augmentation to the minority classes in order to increase the number of MRI images in the dataset. All experiments were conducted using Alzheimer's MRI dataset consisting of brain MRI scanned images. The performance of the proposed model determines detection of the four stages of AD. Experimental results show high performance of the proposed model in that the model achieved a 99.38% accuracy rate, which is the highest so far. Moreover, the proposed model performance in terms of accuracy, precision, sensitivity, specificity, and f-measures is promising when compared to the very recent state-of-the-art domain-specific models existing in the literature.

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Section: Previous Workmentioning

confidence: 99%

“…Similarly, [21] used a hybrid classical-quantum NN model in which the feature extraction was based on CNN-based ResNet34 that converted the given input into 512 feature vectors, and the dimensionality reduction was performed using a quantum variational circuit. Data augmentation was not performed.…”

Section: Cnn From Scratchmentioning

confidence: 99%

A Deep Learning for Alzheimer’s Stages Detection Using Brain Images

Ullah¹,

Jamjoom²

2023

Computers, Materials &Amp; Continua

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“…The symptoms of PD and Alzheimer’s disease are dementia, anxiety, fear, and sleeping problems [ 151 ]. Neurological diseases can cause hallucinations and delusions, which are psychiatric symptoms [ 159 ]. COVID-19 considerably increases motor and non-motor symptoms in PD.…”

Section: The Relationship Between Parkinson’s Disease Heart Brain And...mentioning

confidence: 99%

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review

Suri¹,

Maindarkar

Sauseng

et al. 2022

Diagnostics

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Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID-19 causes the ML systems to become severely non-linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well-explained ML paradigms. Deep neural networks are powerful learning machines that generalize non-linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID-19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID-19 framework. We study the hypothesis that PD in the presence of COVID-19 can cause more harm to the heart and brain than in non-COVID-19 conditions. COVID-19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID-19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID-19 lesions, office and laboratory arterial atherosclerotic image-based biomarkers, and medicine usage for the PD patients for the design of DL point-based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID-19 environment and this was also verified. DL architectures like long short-term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID-19.Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID-19.

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“…Neuroimaging modalities, for instance Magnetic Resonance Imaging (MRI) [5] and Positron Emission Tomography (PET) [6], are playing a significant role in the recognition of AD, with several other biomarkers deployed in clinical practice [7]. PET imaging can detect Aβ deposition in the brain as well as τ-injury caused by τ plaques while neurodegeneration can be detected by the structural MRI modality.…”

Section: Introductionmentioning

confidence: 99%

Early-Stage Alzheimer’s Disease Categorization Using PET Neuroimaging Modality and Convolutional Neural Networks in the 2D and 3D Domains

Tufail

Anwar

Othman

et al. 2022

Sensors

Self Cite

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Alzheimer’s Disease (AD) is a health apprehension of significant proportions that is negatively impacting the ageing population globally. It is characterized by neuronal loss and the formation of structures such as neurofibrillary tangles and amyloid plaques in the early as well as later stages of the disease. Neuroimaging modalities are routinely used in clinical practice to capture brain alterations associated with AD. On the other hand, deep learning methods are routinely used to recognize patterns in underlying data distributions effectively. This work uses Convolutional Neural Network (CNN) architectures in both 2D and 3D domains to classify the initial stages of AD into AD, Mild Cognitive Impairment (MCI) and Normal Control (NC) classes using the positron emission tomography neuroimaging modality deploying data augmentation in a random zoomed in/out scheme. We used novel concepts such as the blurring before subsampling principle and distant domain transfer learning to build 2D CNN architectures. We performed three binaries, that is, AD/NC, AD/MCI, MCI/NC and one multiclass classification task AD/NC/MCI. The statistical comparison revealed that 3D-CNN architecture performed the best achieving an accuracy of 89.21% on AD/NC, 71.70% on AD/MCI, 62.25% on NC/MCI and 59.73% on AD/NC/MCI classification tasks using a five-fold cross-validation hyperparameter selection approach. Data augmentation helps in achieving superior performance on the multiclass classification task. The obtained results support the application of deep learning models towards early recognition of AD.

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

Cited by 40 publications

References 41 publications

A Deep Learning for Alzheimer’s Stages Detection Using Brain Images

A Deep Learning for Alzheimer’s Stages Detection Using Brain Images

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review

Early-Stage Alzheimer’s Disease Categorization Using PET Neuroimaging Modality and Convolutional Neural Networks in the 2D and 3D Domains

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