61 research outputs found
Reply to Nicholas et al. Using a ResNet-18 network to detect features of Alzheimer’s disease on functional magnetic resonance imaging: a failed replication. Comment on “Odusami et al. Analysis of features of Alzheimer’s disease: detection of early stage from functional brain changes in magnetic resonance images using a finetuned ResNet18 Network. Diagnostics 2021, 11, 1071” /
No full textWe have studied the manuscript of Nicholas et al. [1] very attentively; here are our comments: The authors have used a different dataset (ADNI-3, rather than ADNI-2 used in [2]). The protocols of ADNI-2 and ADNI-3 datasets are not fully consistent [3]. The ADNI MR data set includes a wide range of scanner platforms; however, there has been a broad gap between older MRI systems and the state-of-the-art systems within each vendor’s product line. In ADNI-3, the “ADNI 3 Basic” and “ADNI 3 Advanced” protocols were used. The authors failed to mention if the images they used were made using a protocol compatible with ADNI-2. The dMRI spatial resolution was improved between ADNI-2 and ADNI-3 by reducing the voxel size from 2.7 x 2.7 x 2.7 mm to 2.0 x 2.0 x 2.0 mm [4]. This may have influenced the results. Moreover, the classification results among these studies are not directly comparable, because they differ in terms of the sets of participants. We fully agree that the replication of important findings by multiple independent investigators is fundamental to the accumulation of scientific evidence [5]. Deep learning network models are notoriously known for being difficult to replicate, even if the same sets of parameters are used. The training of neural network models is not deterministic, so the models are likely to produce differing results [6]. The strive of the authors to precisely replicate the results may not be achievable. [...]
Pareto Optimized Adaptive Learning with Transposed Convolution for Image Fusion Alzheimer’s Disease Classification
No full textAlzheimer’s disease (AD) is a neurological condition that gradually weakens the brain and impairs cognition and memory. Multimodal imaging techniques have become increasingly important in the diagnosis of AD because they can help monitor disease progression over time by providing a more complete picture of the changes in the brain that occur over time in AD. Medical image fusion is crucial in that it combines data from various image modalities into a single, better-understood output. The present study explores the feasibility of employing Pareto optimized deep learning methodologies to integrate Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) images through the utilization of pre-existing models, namely the Visual Geometry Group (VGG) 11, VGG16, and VGG19 architectures. Morphological operations are carried out on MRI and PET images using Analyze 14.0 software and after which PET images are manipulated for the desired angle of alignment with MRI image using GNU Image Manipulation Program (GIMP). To enhance the network’s performance, transposed convolution layer is incorporated into the previously extracted feature maps before image fusion. This process generates feature maps and fusion weights that facilitate the fusion process. This investigation concerns the assessment of the efficacy of three VGG models in capturing significant features from the MRI and PET data. The hyperparameters of the models are tuned using Pareto optimization. The models’ performance is evaluated on the ADNI dataset utilizing the Structure Similarity Index Method (SSIM), Peak Signal-to-Noise Ratio (PSNR), Mean-Square Error (MSE), and Entropy (E). Experimental results show that VGG19 outperforms VGG16 and VGG11 with an average of 0.668, 0.802, and 0.664 SSIM for CN, AD, and MCI stages from ADNI (MRI modality) respectively. Likewise, an average of 0.669, 0.815, and 0.660 SSIM for CN, AD, and MCI stages from ADNI (PET modality) respectively
Analysis of Features of Alzheimer’s Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network
No full textOne of the first signs of Alzheimer’s disease (AD) is mild cognitive impairment (MCI), in which there are small variants of brain changes among the intermediate stages. Although there has been an increase in research into the diagnosis of AD in its early levels of developments lately, brain changes, and their complexity for functional magnetic resonance imaging (fMRI), makes early detection of AD difficult. This paper proposes a deep learning-based method that can predict MCI, early MCI (EMCI), late MCI (LMCI), and AD. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) fMRI dataset consisting of 138 subjects was used for evaluation. The finetuned ResNet18 network achieved a classification accuracy of 99.99%, 99.95%, and 99.95% on EMCI vs. AD, LMCI vs. AD, and MCI vs. EMCI classification scenarios, respectively. The proposed model performed better than other known models in terms of accuracy, sensitivity, and specificity
An Intelligent System for Early Recognition of Alzheimer’s Disease Using Neuroimaging
No full textAlzheimer’s disease (AD) is a neurodegenerative disease that affects brain cells, and mild cognitive impairment (MCI) has been defined as the early phase that describes the onset of AD. Early detection of MCI can be used to save patient brain cells from further damage and direct additional medical treatment to prevent its progression. Lately, the use of deep learning for the early identification of AD has generated a lot of interest. However, one of the limitations of such algorithms is their inability to identify changes in the functional connectivity in the functional brain network of patients with MCI. In this paper, we attempt to elucidate this issue with randomized concatenated deep features obtained from two pre-trained models, which simultaneously learn deep features from brain functional networks from magnetic resonance imaging (MRI) images. We experimented with ResNet18 and DenseNet201 to perform the task of AD multiclass classification. A gradient class activation map was used to mark the discriminating region of the image for the proposed model prediction. Accuracy, precision, and recall were used to assess the performance of the proposed system. The experimental analysis showed that the proposed model was able to achieve 98.86% accuracy, 98.94% precision, and 98.89% recall in multiclass classification. The findings indicate that advanced deep learning with MRI images can be used to classify and predict neurodegenerative brain diseases such as AD
Optimized convolutional fusion for multimodal neuroimaging in Alzheimer's disease diagnosis: enhancing data integration and feature extraction /
No full textMultimodal neuroimaging has gained traction in Alzheimer's Disease (AD) diagnosis by integrating information from multiple imaging modalities to enhance classification accuracy. However, effectively handling heterogeneous data sources and overcoming the challenges posed by multiscale transform methods remains a significant hurdle. This article proposes a novel approach to address these challenges. To harness the power of diverse neuroimaging data, we employ a strategy that leverages optimized convolution techniques. These optimizations include varying kernel sizes and the incorporation of instance normalization, both of which play crucial roles in feature extraction from magnetic resonance imaging (MRI) and positron emission tomography (PET) images. Specifically, varying kernel sizes allow us to adapt the receptive field to different image characteristics, enhancing the model's ability to capture relevant information. Furthermore, we employ transposed convolution, which increases spatial resolution of feature maps, and it is optimized with varying kernel sizes and instance normalization. This heightened resolution facilitates the alignment and integration of data from disparate MRI and PET data. The use of larger kernels and strides in transposed convolution expands the receptive field, enabling the model to capture essential cross-modal relationships. Instance normalization, applied to each modality during the fusion process, mitigates potential biases stemming from differences in intensity, contrast, or scale between modalities. This enhancement contributes to improved model performance by reducing complexity and ensuring robust fusion. The performance of the proposed fusion method is assessed on three distinct neuroimaging datasets, which include: Alzheimer's Disease Neuroimaging Initiative (ADNI), consisting of 50 participants each at various stages of AD for both MRI and PET (Cognitive Normal, AD, and Early Mild Cognitive); Open Access Series of Imaging Studies (OASIS), consisting of 50 participants each at various stages of AD for both MRI and PET (Cognitive Normal, Mild Dementia, Very Mild Dementia); and whole-brain atlas neuroimaging (AANLIB) (consisting of 50 participants each at various stages of AD for both MRI and PET (Cognitive Normal, AD). To evaluate the quality of the fused images generated via our method, we employ a comprehensive set of evaluation metrics, including Structural Similarity Index Measurement (SSIM), which assesses the structural similarity between two images; Peak Signal-to-Noise Ratio (PSNR), which measures how closely the generated image resembles the ground truth; Entropy (E), which assesses the amount of information preserved or lost during fusion; the Feature Similarity Indexing Method (FSIM), which assesses the structural and feature similarities between two images; and Edge-Based Similarity (EBS), which measures the similarity of edges between the fused and ground truth images. The obtained fused image is further evaluated using a Mobile Vision Transformer. In the classification of AD vs. Cognitive Normal, the model achieved an accuracy of 99.00%, specificity of 99.00%, and sensitivity of 98.44% on the AANLIB dataset
Explainable Deep-Learning-Based Diagnosis of Alzheimer’s Disease Using Multimodal Input Fusion of PET and MRI Images
No full textPurpose: Alzheimer’s disease (AD) is a progressive, incurable human brain illness that impairs reasoning and retention as well as recall. Detecting AD in its preliminary stages before clinical manifestations is crucial for timely treatment. Magnetic Resonance Imaging (MRI) provides valuable insights into brain abnormalities by measuring the decrease in brain volume expressly in the mesial temporal cortex and other regions of the brain, while Positron Emission Tomography (PET) measures the decrease of glucose concentration in the temporoparietal association cortex. When these data are combined, the performance of AD diagnostic methods could be improved. However, these data are heterogeneous and there is a need for an effective model that will harness the information from both data for the accurate prediction of AD. Methods: To this end, we present a novel heuristic early feature fusion framework that performs the concatenation of PET and MRI images, while a modified Resnet18 deep learning architecture is trained simultaneously on the two datasets. The innovative 3-in-channel approach is used to learn the most descriptive features of fused PET and MRI images for effective binary classification of AD. Results: The experimental results show that the proposed model achieved a classification accuracy of 73.90% on the ADNI database. Then, we provide an Explainable Artificial Intelligence (XAI) model, allowing us to explain the results. Conclusion: Our proposed model could learn latent representations of multimodal data even in the presence of heterogeneity data; hence, the proposed model partially solved the issue with the heterogeneity of the MRI and PET data
Pixel-Level Fusion Approach with Vision Transformer for Early Detection of Alzheimer’s Disease
No full textAlzheimer’s disease (AD) has become a serious hazard to human health in recent years, and proper screening and diagnosis of AD remain a challenge. Multimodal neuroimaging input can help identify AD in the early mild cognitive impairment (EMCI) and late mild cognitive impairment (LMCI) stages from normal cognitive development using magnetic resonance imaging (MRI) and positron emission tomography (PET). MRI provides useful information on brain structural abnormalities, while PET data provide the difference between physiological and pathological changes in brain anatomy. The precision of diagnosing AD can increase when these data are combined. However, they are heterogeneous and appropriate, and an adequate number of features are required for AD classification. This paper proposed a multimodal fusion-based approach that uses a mathematical technique called discrete wavelet transform (DWT) to analyse the data, and the optimisation of this technique is achieved through transfer learning using a pre-trained neural network called VGG16. The final fused image is reconstructed using inverse discrete wavelet transform (IDWT). The fused images are classified using a pre-trained vision transformer. The evaluation of the benchmark Alzheimer’s disease neuroimaging initiative (ADNI) dataset shows an accuracy of 81.25% for AD/EMCI and AD/LMCI in MRI test data, as well as 93.75% for AD/EMCI and AD/LMCI in PET test data. The proposed model performed better than existing studies when tested on PET data with an accuracy of 93.75%
An Improved Model for Securing Ambient Home Network against Spoofing Attack
No full textMobile Ad hoc Networks (MANET) are prone to malicious attacks and intermediate nodes on the home network may spoof the packets being transmitted before reaching the destination. This study implements an enhanced Steganography Adaptive Neuro-Fuzzy Algorithm (SANFA) technique for securing the ambient home network against spoofing attacks. Hybrid techniques that comprises image steganography, adaptive neuro-fuzzy and transposition cipher were used for the model development. Two variants of the model: SANFA and transpose SANFA were compared using precision and convergence time as performance metrics. The simulation results showed that the transpose SANFA has lower percentage of precision transmitting in a smaller network and a higher percentage of precision transmitting in a larger network. The convergence time result showed that packet transmitted in a smaller network size took longer time to converge while packet transmitted in a larger network size took shorter period to converge
Machine learning with multimodal neuroimaging data to classify stages of Alzheimer’s disease: a systematic review and meta-analysis
No full textIn recent years, Alzheimer’s disease (AD) has been a serious threat to human health. Researchers and clinicians alike encounter a significant obstacle when trying to accurately identify and classify AD stages. Several studies have shown that multimodal neuroimaging input can assist in providing valuable insights into the structural and functional changes in the brain related to AD. Machine learning (ML) algorithms can accurately categorize AD phases by identifying patterns and linkages in multimodal neuroimaging data using powerful computational methods. This study aims to assess the contribution of ML methods to the accurate classification of the stages of AD using multimodal neuroimaging data. A systematic search is carried out in IEEE Xplore, Science Direct/Elsevier, ACM DigitalLibrary, and PubMed databases with forward snowballing performed on Google Scholar. The quantitative analysis used 47 studies. The explainable analysis was performed on the classification algorithm and fusion methods used in the selected studies. The pooled sensitivity and specificity, including diagnostic efficiency, were evaluated by conducting a meta-analysis based on a bivariate model with the hierarchical summary receiver operating characteristics (ROC) curve of multimodal neuroimaging data and ML methods in the classification of AD stages. Wilcoxon signed-rank test is further used to statistically compare the accuracy scores of the existing models. With a 95% confidence interval of 78.87–87.71%, the combined sensitivity for separating participants with mild cognitive impairment (MCI) from healthy control (NC) participants was 83.77%; for separating participants with AD from NC, it was 94.60% (90.76%, 96.89%); for separating participants with progressive MCI (pMCI) from stable MCI (sMCI), it was 80.41% (74.73%, 85.06%). With a 95% confidence interval (78.87%, 87.71%), the Pooled sensitivity for distinguishing mild cognitive impairment (MCI) from healthy control (NC) participants was 83.77%, with a 95% confidence interval (90.76%, 96.89%), the Pooled sensitivity for distinguishing AD from NC was 94.60%, likewise (MCI) from healthy control (NC) participants was 83.77% progressive MCI (pMCI) from stable MCI (sMCI) was 80.41% (74.73%, 85.06%), and early MCI (EMCI) from NC was 86.63% (82.43%, 89.95%). Pooled specificity for differentiating MCI from NC was 79.16% (70.97%, 87.71%), AD from NC was 93.49% (91.60%, 94.90%), pMCI from sMCI was 81.44% (76.32%, 85.66%), and EMCI from NC was 85.68% (81.62%, 88.96%). The Wilcoxon signed rank test showed a low P-value across all the classification tasks. Multimodal neuroimaging data with ML is a promising future in classifying the stages of AD but more research is required to increase the validity of its application in clinical practice
Cloud Ownership and Reliability – Issues and Developments
No full textCloud computing is a composite paradigm that provides crucial
services to individuals and organisations over networked infrastructure at a cost.
The Cloud provides custom built applications, made available by a CSP to
customers. Several customers can access an instance of one application. The
Cloud also affords an avenue for customers to build their own application in a
language compatible with a CSP and subsequently deploy that application on
the Cloud. In addition, massive scalable storage and computing devices are
available on the Cloud. A customers expects optimum services whenever and
wherever it is required. Hence, system failure on the part of a CSP must not
affect the services being provided to the customer. This paper examines present
trends in the area of Cloud ownership reliability and provides a guide for future
research. The paper aims to answer the following question: what is the current
trend and development in Cloud ownership reliability? In addition, analysis was
done on existing work published in journals, conferences, white papers and
those published in reputable magazines, to answer the question raised. The
expected result is the identification of trends in Cloud ownership and reliability
which will be of benefit to prospective Cloud users and service providers alike
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