Relieff Feature Selection Based Alzheimer Disease Classification Using Hybrid Features and Support Vector Machine in Magnetic Resonance Imaging

Suresha, Halebedu Subbaraya; Parthasarathi, Sathyan

doi:10.34218/ijcet.10.1.2019.015

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…In order to identify certain patterns in the dataset, image classification requires the extraction of features from a picture. It may end up costing a lot of money to compute to use an ANN for photo classification because of the relatively large adaptable components [17]. Figure 1 depicts the functional and common building block of Convent.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Comparative Analysis of CNN and Different R-CNN based Model for Prediction of Alzheimer’s Disease

Roobini,

Kavitha,

Karthik

2024

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Medical images still need to be examined by medical personnel, which is a prolonged and vulnerable progression. The dataset used included 4 classes of 6400 training and test MRI images each and was collected from Kaggle such as cognitively normal (CN), Mild Cognitive Impairment stage (MCI), moderate cognitive impairment (Moderate MCI), and Severe stage of cognitive impairment (AD). OBJECTIVES: There was a glaring underrepresentation of the Alzheimer Disease (AD) class. The accuracy and effectiveness of diagnoses can be improved with the use of neural network models. METHODS: In order to establish which CNN-based algorithm performed the multi-class categorization of the AD patient's brain MRI images most accurately. Thus, examine the effectiveness of the popular CNN-based algorithms like Convolutional Neural Network (CNN), Region-based CNN (R-CNN), Fast R-CNN, and Faster R-CNN. RESULTS: On the confusion matrix, R-CNN performed the best. CONCLUSION: R-CNN is quick and offers a high precision of 98.67% with a low erroneous measure of 0.0133, as shown in the research.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

Comparative Analysis of CNN and Different R-CNN based Model for Prediction of Alzheimer’s Disease

Roobini,

Kavitha,

Karthik

2024

EAI Endorsed Trans Perv Health Tech

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show abstract

“…Third, the algorithm of ReliefF was applied to determine the most critical cortex characteristics from the significant cortex characteristics mentioned above (35). This process was performed because the ReliefF could detect the context information among features, and the weights of WCC were sorted according to the relevance between features and categories (36).…”

Section: Phenotypic Computationmentioning

confidence: 99%

Revealing the correlations between brain cortical characteristics and susceptibility genes for Alzheimer disease: a cross-sectional study

Han¹,

Jiang²,

Yao³

et al. 2023

Quant Imaging Med Surg

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Background: Alzheimer disease (AD) is a progressive neurodegenerative disease closely related to genes and characterized by the atrophy of the cerebral cortex. Correlations between imaging phenotypes and the susceptibility genes for AD, as demonstrated in the findings of genome-wide association studies (GWASs), still need to be addressed due to the complicated structure of the human cortex.Methods: In our study, an improved GWAS method, whole cortex characteristics GWAS (WCC-GWAS), was proposed. The WCC-GWAS uses multiple cortex characteristics of gray-matter volume (GMV), cortical thickness (CT), cortical surface area (CSA), and local gyrification index (LGI). A cohort of 496 participants was enrolled and divided into 4 groups: normal control (NC; n=122), early mild cognitive impairment (EMCI; n=196), late mild cognitive impairment (LMCI; n=62), and AD (n=116). Based on the Desikan-Killiany atlas, the brain was parcellated into 68 brain regions, and the WCC of each brain region was individually calculated. Four cortex characteristics of GMV, CT, CSA, and LGI across the 4 groups optimized with multiple comparisons and the ReliefF algorithm were taken as magnetic resonance imaging (MRI) brain phenotypes. Under the model of multiple linear additive genetic regression, the correlations between the MRI brain phenotypes and single-nucleotide polymorphisms (SNPs) were deduced. Results:The findings identified 2 prominent correlations. First, rs7309929 of neuron navigator 3 (NAV3) located on chromosome 12 correlated with the decreased GMV for the left middle temporal gyrus (P=0.0074).Second, rs11250992 of long intergenic non-protein-coding RNA 700 (LINC00700) located on chromosome 10 correlated with the decreased CT for the left supramarginal gyrus (P=0.0019). Conclusions:The findings suggested that the correlations between phenotypes and genotypes could be effectively evaluated. The strategy of extracting MRI phenotypes as endophenotypes provided valuable indications in AD GWAS.

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A Survey on Alzheimer’s Disease Detection and Classification

Mane¹,

Patel²,

Sawant³

et al. 2021

Data Engineering for Smart Systems

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Relieff Feature Selection Based Alzheimer Disease Classification Using Hybrid Features and Support Vector Machine in Magnetic Resonance Imaging

Cited by 4 publications

References 17 publications

Comparative Analysis of CNN and Different R-CNN based Model for Prediction of Alzheimer’s Disease

Comparative Analysis of CNN and Different R-CNN based Model for Prediction of Alzheimer’s Disease

Revealing the correlations between brain cortical characteristics and susceptibility genes for Alzheimer disease: a cross-sectional study

A Survey on Alzheimer’s Disease Detection and Classification

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