Alzheimer’s Disease Detection from Fused PET and MRI Modalities Using an Ensemble Classifier

Shukla, Amar; Tiwari, Rajeev; Tiwari, Shamik

doi:10.3390/make5020031

Cited by 16 publications

(3 citation statements)

References 57 publications

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“…Accurate diagnosis of AD, a brain condition that causes neurodegeneration, requires imaging techniques. 8 These systems can detect AD biomarkers from sMRI and categorize brain images into three groups: CN, MCI, and AD. One notable advancement in this field is the 3DMgNet architecture, which has achieved a 92.133% A cc rate in classifying AD versus NC while reducing the number of model parameters.…”

Section: Related Studymentioning

confidence: 99%

Structural biomarker‐based Alzheimer's disease detection via ensemble learning techniques

Shukla,

Tiwari,

Tiwari

2023

Int J Imaging Syst Tech

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Alzheimer's disease (AD) is a degenerative neurological disorder with incurable characteristics. To identify the substantial solution, we used a structural biomarker (structural magnetic resonance imaging) to see the neurostructural changes in the different regions of the brain of AD, mild cognitive impairment, and cognitive normal subjects. In this study, we detected the AD and their subtypes by using the traditional machine learning and ensemble learning models. It is also identified the relative impact score of various cortical and subcortical regions of AD and their subtypes. Experimental study contains two levels of classification: binary and multiclass. The Ensemble_LR_SVM model in binary classification has 99% accuracy in detection. Random forest model in the multiclass has 82% of accuracy. In the cortical‐subcortical analysis, the right hemisphere's parahippocampal and entorhinal regions were discovered to be the most influential. Similarly, the inferior temporal and isthmus cingulate regions had a significant influence in the left hemisphere.

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Section: Related Studymentioning

confidence: 99%

Structural biomarker‐based Alzheimer's disease detection via ensemble learning techniques

Shukla,

Tiwari,

Tiwari

2023

Int J Imaging Syst Tech

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“…In recent years, there has been a growing interest in leveraging multimodal neuroimaging data to enhance the accuracy of AD classification. Combining information from multiple imaging modalities can provide a more comprehensive understanding of AD [7,[15][16][17] in capturing complementary aspects of brain alterations that may not be evident in a single modality. However, effectively integrating these heterogeneous data sources presents a considerable challenge.…”

Section: Introductionmentioning

confidence: 99%

“…Likewise, researchers have classified individuals with AD according to their GM density and glucose utilization from MRI and PET, allowing for a more comprehensive and accurate diagnosis of AD [19]. Furthermore, a novel multimodal image-fusion technique designed to merge PET and MRI data was introduced, in which the extracted features are subsequently input into an ensemble classifier [17]. While the automatic pipeline method described in their study utilized techniques such as Free Surfer and affine registration for pixel-level fusion, achieving precise alignment and ensuring that the combined information accurately reflects the underlying neurobiological changes was a challenge.…”

Section: Introductionmentioning

confidence: 99%

Optimized Convolutional Fusion for Multimodal Neuroimaging in Alzheimer’s Disease Diagnosis: Enhancing Data Integration and Feature Extraction

Odusami,

Maskeliūnas,

Damaševičius

2023

JPM

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Multimodal 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.

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Multimodal Neuroimaging Fusion for Alzheimer's Disease: An Image Colorization Approach With Mobile Vision Transformer

Odusami,

Damasevicius,

Milieskaite‐Belousoviene

et al. 2024

Int J Imaging Syst Tech

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Multimodal neuroimaging, combining data from different sources, has shown promise in the classification of the Alzheimer's disease (AD) stage. Existing multimodal neuroimaging fusion methods exhibit certain limitations, which require advancements to enhance their objective performance, sensitivity, and specificity for AD classification. This study uses the use of a Pareto‐optimal cosine color map to enhance classification performance and visual clarity of fused images. A mobile vision transformer (ViT) model, incorporating the swish activation function, is introduced for effective feature extraction and classification. Fused images from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the Whole Brain Atlas (AANLIB), and Open Access Series of Imaging Studies (OASIS) datasets, obtained through optimized transposed convolution, are utilized for model training, while evaluation is achieved using images that have not been fused from the same databases. The proposed model demonstrates high accuracy in AD classification across different datasets, achieving 98.76% accuracy for Early Mild Cognitive Impairment (EMCI) versus LMCI, 98.65% for Late Mild Cognitive Impairment (LMCI) versus AD, 98.60% for EMCI versus AD, and 99.25% for AD versus Cognitive Normal (CN) in the ADNI dataset. Similarly, on OASIS and AANLIB, the precision of the AD versus CN classification is 99.50% and 96.00%, respectively. Evaluation metrics showcase the model's precision, recall, and F1 score for various binary classifications, emphasizing its robust performance.

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Alzheimer’s Disease Detection from Fused PET and MRI Modalities Using an Ensemble Classifier

Cited by 16 publications

References 57 publications

Structural biomarker‐based Alzheimer's disease detection via ensemble learning techniques

Structural biomarker‐based Alzheimer's disease detection via ensemble learning techniques

Optimized Convolutional Fusion for Multimodal Neuroimaging in Alzheimer’s Disease Diagnosis: Enhancing Data Integration and Feature Extraction

Multimodal Neuroimaging Fusion for Alzheimer's Disease: An Image Colorization Approach With Mobile Vision Transformer

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