Classification of Initial Stages of Alzheimer’s Disease through Pet Neuroimaging Modality and Deep Learning: Quantifying the Impact of Image Filtering Approaches

Tufail, Ahsan Bin; Ma, Yongkui; Kaabar, Mohammed K. A.; Rehman, Ateeq Ur; Khan, Rahim; Cheikhrouhou, Omar

doi:10.3390/math9233101

Cited by 14 publications

(8 citation statements)

References 57 publications

(55 reference statements)

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“…Out-of-set generalization had remained a challenge for Computer Vision, which has been resolved with the availability of recurrence and feedforward learning networks. DNNs can be used to find hidden patterns from the data and learn the classification decision boundaries concurrently, thus skipping the tedious step of feature engineering, making them a viable choice for medical imaging modalities [8]. In [9], it was reported that rather than training an entire model from the start, it is better to begin from an already-trained deep neural network and then adjust the last layers for the medical image dataset.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…As a result, they may be unable to handle more complicated cases where spine pathologies and curvatures are present. In recent years, deep learning has become a research hotspot in medical image analysis [18] because of its high feature extraction ability [19][20][21][22][23][24]. Deep neural networks (DNNs) often use successful tools as an extractor of high-level features.…”

Section: Introductionmentioning

confidence: 99%

SVseg: Stacked Sparse Autoencoder-Based Patch Classification Modeling for Vertebrae Segmentation

et al. 2022

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Precise vertebrae segmentation is essential for the image-related analysis of spine pathologies such as vertebral compression fractures and other abnormalities, as well as for clinical diagnostic treatment and surgical planning. An automatic and objective system for vertebra segmentation is required, but its development is likely to run into difficulties such as low segmentation accuracy and the requirement of prior knowledge or human intervention. Recently, vertebral segmentation methods have focused on deep learning-based techniques. To mitigate the challenges involved, we propose deep learning primitives and stacked Sparse autoencoder-based patch classification modeling for Vertebrae segmentation (SVseg) from Computed Tomography (CT) images. After data preprocessing, we extract overlapping patches from CT images as input to train the model. The stacked sparse autoencoder learns high-level features from unlabeled image patches in an unsupervised way. Furthermore, we employ supervised learning to refine the feature representation to improve the discriminability of learned features. These high-level features are fed into a logistic regression classifier to fine-tune the model. A sigmoid classifier is added to the network to discriminate the vertebrae patches from non-vertebrae patches by selecting the class with the highest probabilities. We validated our proposed SVseg model on the publicly available MICCAI Computational Spine Imaging (CSI) dataset. After configuration optimization, our proposed SVseg model achieved impressive performance, with 87.39% in Dice Similarity Coefficient (DSC), 77.60% in Jaccard Similarity Coefficient (JSC), 91.53% in precision (PRE), and 90.88% in sensitivity (SEN). The experimental results demonstrated the method’s efficiency and significant potential for diagnosing and treating clinical spinal diseases.

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“…Machine and deep learning (ML/DL) techniques have received substantial attention for the assessment of data from different sets of inputs such as text, images or volumes for different applications such as depression recognition [8], opinion leader identification [9], multi-object fuse detection [10], AD classification [11][12][13], cancer prediction [14], joint Alzheimer's and Parkinson's diseases classification [15,16], automatic modulation classification [17,18], diabetic retinopathy classification [19], AD assessment using independent component analysis technique [20], and endangered plant species recognition [21]. These methods can optimally infer representations from raw data through the use of a stratified sampling approach with many varying levels of intricacies.…”

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

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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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Classification of Initial Stages of Alzheimer’s Disease through Pet Neuroimaging Modality and Deep Learning: Quantifying the Impact of Image Filtering Approaches

Cited by 14 publications

References 57 publications

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

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

SVseg: Stacked Sparse Autoencoder-Based Patch Classification Modeling for Vertebrae Segmentation

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

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