33 hasil untuk "Goo-Rak Kwon"
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Artikel Jurnal
Convolutional Neural Network-Based Natural Image and MRI Classification Using Gaussian Activated Parametric (GAP) Layer
We propose a novel gaussian activated parametric (GAP) layer for deep neural networks specifically for CNN. This layer normalizes the feature vector using the Gaussian filter-based un-sharpening technique. The goal of the proposed method is to normalize and activate the initial and intermediate…
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Artikel Jurnal
Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield and Amygdala Volume of Structural MRI
Accurate diagnosis of the initial phase of Alzheimer’s disease (AD) is essential and crucial. The objective of this research was to employ efficient biomarkers for the diagnostic analysis and classification of AD based on combining structural MRI (sMRI) and resting-state functional MRI (rs-fMRI)…
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Artikel Jurnal
3D CNN Design for the Classification of Alzheimer’s Disease Using Brain MRI and PET
Attempt to diagnose Alzheimer's disease (AD) using imaging modalities is one of the scopes of deep learning. While considering the theoretical background from past studies, we are trying to identify convolutional neural network (CNN) behaviors moving from 2D to 3D architecture. This study aims to…
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Artikel Jurnal Teks lengkap
Classification of Alzheimer’s Disease and Mild-Cognitive Impairment Base on High-Order Dynamic Functional Connectivity at Different Frequency Band
Functional brain connectivity networks obtained from resting-state functional magnetic resonance imaging (rs-fMRI) have been extensively utilized for the diagnosis of Alzheimer’s disease (AD). However, the traditional correlation analysis technique only explores the pairwise relation, which may not…
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Artikel Jurnal Teks lengkap
Dissimilarity Corrective Generative Adversarial Network for Brain Image Segmentation
More accurate diagnosis of brain disorders can be achieved by properly analyzing structural changes in the brain. For the quantification of change in brain structure, the segmentation task is crucial. Recently, generative adversarial networks (GAN) have been rapidly developed and used in many…
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Artikel Jurnal
A novel scaled-gamma-tanh (SGT) activation function in 3D CNN applied for MRI classification
Abstract Activation functions in the neural network are responsible for ‘firing’ the nodes in it. In a deep neural network they ‘activate’ the features to reduce feature redundancy and learn the complex pattern by adding non-linearity in the network to learn task-specific goals. In this paper, we…
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Artikel Jurnal
3D CNN based Alzheimer’s diseases classification using segmented Grey matter extracted from whole-brain MRI
A recent study from MRI has revealed that there is a minor increase in cerebral-spinal fluid (CSF) content in brain ventricles and sulci, along with a substantial decrease in grey matter (GM) content and brain volume among Alzheimer's disease (AD) patients. It has been discovered that the grey…
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Artikel Jurnal Teks lengkap
Explainable Vision Transformer with Self-Supervised Learning to Predict Alzheimer’s Disease Progression Using 18F-FDG PET
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects millions of people worldwide. Early and accurate prediction of AD progression is crucial for early intervention and personalized treatment planning. Although AD does not yet have a reliable therapy, several…
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Artikel Jurnal Teks lengkap
SIP-UNet: Sequential Inputs Parallel UNet Architecture for Segmentation of Brain Tissues from Magnetic Resonance Images
Proper analysis of changes in brain structure can lead to a more accurate diagnosis of specific brain disorders. The accuracy of segmentation is crucial for quantifying changes in brain structure. In recent studies, UNet-based architectures have outperformed other deep learning architectures in…
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Artikel Jurnal
Unsupervised Visual Anomaly Detection Using Self-Supervised Pre-Trained Transformer
In the various industrial manufacturing processes, the automatic visual inspection system is an essential part as it reduces the chances of delivering defective products and the cost of training and hiring experts for manual inspection. In this work, we propose a new unsupervised anomaly detection…
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Artikel Jurnal
Diagnosis of Alzheimer’s Disease Using Brain Network
Recent studies suggest the brain functional connectivity impairment is the early event occurred in case of Alzheimer’s disease (AD) as well as mild cognitive impairment (MCI). We model the brain as a graph based network to study these impairment. In this paper, we present a new diagnosis approach…
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Artikel Jurnal Teks lengkap
Image-Level Anti-Personnel Landmine Detection Using Deep Learning in Long-Wave Infrared Images
This study proposes a simple deep learning-based framework for image-level anti-personnel landmine detection in long-wave infrared imagery. To address challenges posed by the limited size of the available dataset and the small spatial size of anti-personnel landmines within images, we integrate two…
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APOE ε4 Allele Dose and Time to Clinical Conversion from Mild Cognitive Impairment to Alzheimer’s Disease Dementia: An ADNI Survival Analysis
Background/Objectives: Existing Alzheimer’s disease (AD) prediction studies often treat APOE ε4 as a binary carrier variable and emphasize classification rather than time-to-event progression. This study evaluated whether APOE ε4 allele dose predicts clinical conversion from mild cognitive…
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Artikel Jurnal Teks lengkap
Resting-State Functional Connectivity Difference in Alzheimer’s Disease and Mild Cognitive Impairment Using Threshold-Free Cluster Enhancement
The disruption of functional connectivity is one of the early events that occurs in the brains of Alzheimer’s disease (AD) patients. This paper reports a study on the clustering structure of functional connectivity in eight important brain networks in healthy, AD, and prodromal stage subjects. We…
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Artikel Jurnal
Automated Detection of Alzheimer’s Disease and Mild Cognitive Impairment Using Whole Brain MRI
Early diagnosis is critical for the development and success of interventions, and neuroimaging is one of the most promising areas for early detection of Alzheimer’s disease (AD). This study is aimed to develop a deep learning method to extract valuable AD biomarkers from structural magnetic…
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Artikel Jurnal Teks lengkap
Genetics Information with Functional Brain Networks for Dementia Classification
Mild cognitive impairment (MCI) precedes the Alzheimer’s disease (AD) continuum, making it crucial for therapeutic care to identify patients with MCI at risk of progression. We aim to create generalized models to identify patients with MCI who advance to AD using high-dimensional-data resting state…
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Artikel Jurnal Teks lengkap
Interpretable 2D Deep Learning for Alzheimer’s Detection from sMRI: A Lightweight Residual CNN Approach with Comprehensive Preprocessing and Stratified Data Partitioning
Neuroimaging is a promising modality for early AD detection, facilitating timely clinical intervention. This study proposes an enhanced deep learning framework that extracts critical AD biomarkers from structural MRI (sMRI) data acquired from the ADNI. Our novel CNN architecture integrates…
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Artikel Jurnal Teks lengkap
Deterioration Diagnosis of Solar Module Using Thermal and Visible Image Processing
Several factors cause the output degradation of the photovoltaic (PV) module. The main affecting elements are the higher PV module temperature, the shaded cell, the shortened or conducting bypass diodes, and the soiled and degraded PV array. In this paper, we introduce an image processing technique…
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Artikel Jurnal
Alzheimer’s Disease and Mild Cognitive Impairment Detection Using sMRI With Efficient Receptive Field and Enhanced Multi-Axis Attention Fusion
Deep neural networks have shown promising results in the analysis of structural magnetic resonance imaging (sMRI) data for the diagnosis of dementia, particularly Alzheimer’s disease (AD). Different regions of the brain have diverse structures that are linked to specific functions, which could…
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Artikel Jurnal Teks lengkap
Classification of Alzheimer’s Disease Based on Core-Large Scale Brain Network Using Multilayer Extreme Learning Machine
Various studies suggest that the network deficit in default network mode (DMN) is prevalent in Alzheimer’s disease (AD) and mild cognitive impairment (MCI). Besides DMN, some studies reveal that network alteration occurs in salience network motor networks and large scale network. In this study we…