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  • CNN+ Grad-CAM based Alzheimers detection from MRI scans using ResNet18.
    This project uses a deep learning model (ResNet-18) to classify MRI brain scans and detect Alzheimer's disease Grad-CAM is applied to visualize important brain regions contributing to the model's decisions alzheimers_resnet18_gradcam ├── model # Pretrained ResNet18 model ├── outputs
  • Early Detection of Alzheimers Disease from MR Images Using . . . - Springer
    This study develops an automatic algorithm for detecting Alzheimer's disease (AD) using magnetic resonance imaging (MRI) through deep learning and feature selection techniques It utilizes a dataset of 6400 MRI images from Kaggle, categorized into four classes Initially, the study employs pretrained CNN architectures—DenseNet-201, MobileNet-v2, ResNet-18, ResNet-50, ResNet-101, and
  • Alzheimer’s Disease Detection using 3D ResNet-18 on MRI
    This model detects Alzheimer’s Disease (AD) using the ResNet-18 model on Magnetic Resonance Imaging (MRI) In this model, we propose a method to utilise transfer learning in 3D CNNs, which allows the transfer of knowledge from 2D image datasets (ImageNet) to a 3D image dataset
  • Analysis of Features of Alzheimer’s Disease: Detection of . . . - MDPI
    The ResNet -18 we are using in this study, uses 3 × 3 filters with stride and pad of 1, and the average pooling layer contains 1 × 1 filter, and one fully connected layer, with a final softmax layer Habes, M ; Wolk, D A ; Fan, Y A deep learning model for early prediction of Alzheimer’s disease dementia based on hippocampal MRI arXiv
  • Introducing Transfer Learning to 3D ResNet-18 for Alzheimer’s Disease . . .
    This paper focuses on detecting Alzheimer's Disease (AD) using the ResNet-18 model on Magnetic Resonance Imaging (MRI) Previous studies have applied different 2D Convolutional Neural Networks (CNNs) to detect AD The main idea being to split 3D MRI scans into 2D image slices, so that classification can be performed on the image slices independently This idea allows researchers to benefit
  • Prediction of Alzheimers progression based on multimodal Deep-Learning . . .
    Alzheimer's Progression Prediction using Multimodal Deep Learning-based Fusion and Visual Explainability of Time Series Data In this study, we utilized 3D ResNet18 as the backbone architecture to extract the deep features from each 3D MRI volume The obtained feature maps were utilized to train a BRNN for the progression-detection task
  • Analysis of Features of Alzheimers Disease: Detection of Early Stage . . .
    One 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
  • Adapting to evolving MRI data: A transfer learning approach for . . .
    In our study, we propose a novel TL-based framework for improving AD prediction using evolving MRI data Our approach has been validated on a target dataset comprising a small collection of three-dimensional T1-weighted MRI scans acquired at 3T, a subset from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset (Mueller et al , 2005
  • Using a ResNet-18 Network to Detect Features of Alzheimer’s . . . - MDPI
    2022 "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
  • ResD Hybrid Model Based on Resnet18 and Densenet121 for Early Alzheimer . . .
    Lin, W , et al : Convolutional neural networks-based MRI image analysis for the Alzheimer’s disease prediction from mild cognitive impairment Front Neurosci 12, 777 (2018) Article Google Scholar Mehmood, A , et al : A transfer learning approach for early diagnosis of Alzheimer’s disease on MRI images Neuroscience 460, 43–52 (2021)





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