Alzheimer's Disease Prediction Using Convolutional Neural Network Models Leveraging Pre-existing Architecture and Transfer Learning

Abed, Mahjabeen Tamanna; Fatema, Umme; Nabil, Shanewas Ahmed; Alam, Md. Ashraful; Reza, Tanzim

doi:10.1109/icievicivpr48672.2020.9306649

Cited by 21 publications

(8 citation statements)

References 17 publications

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“…Reference [27] have implemented ResNet50 on 3064 Brain CE-MRIs using only accuracy. For the classification of Alzheimer's disease [28] implemented, ResNet50 and InceptionV3 models on the ADNI dataset. The models were evaluated only for accuracy.…”

Section: Discussionmentioning

confidence: 99%

Data Complexity Based Evaluation of the Model Dependence of Brain MRI Images for Classification of Brain Tumor and Alzheimer’s Disease

et al. 2022

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The convolutional neural networks (CNN) have shown promising results for various classification problems over the past years. However, selecting various CNN architectures is still challenging as each architecture performs differently with the same dataset. This research aims to evaluate the dependence of brain MRI on various predictive models of CNN based on the complexity of the data for Brain Tumor and Alzheimer's Disease. Our proposed approach has three parts. First part is the pre-processing of the data which mainly focuses on class balancing and the estimation of data complexity. The second part uses stratified k-fold cross-validation for more reliable results. The last part corresponds to the implementation of four CNN models applying described methods. This paper compares the classification performance of rigorous experimentation on four CNN variants namely S-CNN (CNN trained from scratch), ResNet50, InceptionV3, and Xception over two brain MRI image datasets evaluated with and without the use of Principal Component Analysis (PCA). The work benchmarks CNN models by comparing the average scores of Accuracy, Precision, Recall, F1 score, and AUC score from the stratified five-fold cross-validation.

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Section: Discussionmentioning

confidence: 99%

Data Complexity Based Evaluation of the Model Dependence of Brain MRI Images for Classification of Brain Tumor and Alzheimer’s Disease

et al. 2022

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“…It is expected to be difficult to optimize the deep-layered architectures but the deeper models are modified to skip connections for avoiding overfitting. 25 It has usually three different levels that comprise convolution, normalization, and ReLu. The special type of interconnection provides an easy way to train the overall network.…”

Section: Methodsmentioning

confidence: 99%

A Class Activation Map-Based Interpretable Transfer Learning Model for Automated Detection of ADHD from fMRI Data

et al. 2022

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Automatic detection of Attention Deficit Hyperactivity Disorder (ADHD) based on the functional Magnetic Resonance Imaging (fMRI) through Deep Learning (DL) is becoming a quite useful methodology due to the curse of-dimensionality problem of the data is solved. Also, this method proposes an invasive and robust solution to the variances in data acquisition and class distribution imbalances. In this paper, a transfer learning approach, specifically ResNet-50 type pre-trained 2D-Convolutional Neural Network (CNN) was used to automatically classify ADHD and healthy children. The results demonstrated that ResNet-50 architecture with 10-k cross-validation (CV) achieves an overall classification accuracy of 93.45%. The interpretation of the results was done via the Class Activation Map (CAM) analysis which showed that children with ADHD differed from controls in a wide range of brain areas including frontal, parietal and temporal lobes.

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“…The TL model leveraged the features obtained from the ImageNet-pretrained AlexNet, and Zaabi et al [42] reported that the TL model provided more accurate results. Abed et al [43] used three DL models (VGG-19, Inception v3, and ResNet-50) to initially train on ImageNet and subsequently fine-tuned on ADNI data to identify AD, MCI, and CN from sMRI images. Because the hippocampus is the initial region of the brain to be impacted by AD, atrophy of the hippocampus can be recognized using sMRI and DTI by preserving the shape, but reversing the appearance.…”

Section: Machine Learning For Ad Detectionmentioning

confidence: 99%

Machine learning for automatic Alzheimer’s disease detection: addressing domain shift issues for building robust models

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Huang

et al. 2023

Radiology Science

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Alzheimer’s disease (AD) is a type of brain disease that affects a person’s ability to perform daily tasks. Modern neuroimaging techniques have made it possible to detect structural and functional changes in the brain that are linked to AD, and machine learning (ML)-based methods have been extensively developed to help physicians achieve fast and accurate imaging-based AD detection. One critical issue when deploying ML methods in clinical applications is the domain shift that exists between the training and test data, which may significantly attenuate a model’s performance. To resolve this issue, domain adaptation (DA) is needed to narrow the performance gap between data from domains with different distributions. The purpose of this review is to offer insight into the state of ML and DA research in the field of neuroimaging-based AD detection. The limitations of existing studies, as well as opportunities for future studies, are discussed with the hope that more investigations will be conducted in the future to optimize the clinical workflow for AD diagnosis and treatment.

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Alzheimer's Disease Prediction Using Convolutional Neural Network Models Leveraging Pre-existing Architecture and Transfer Learning

Cited by 21 publications

References 17 publications

Data Complexity Based Evaluation of the Model Dependence of Brain MRI Images for Classification of Brain Tumor and Alzheimer’s Disease

Data Complexity Based Evaluation of the Model Dependence of Brain MRI Images for Classification of Brain Tumor and Alzheimer’s Disease

A Class Activation Map-Based Interpretable Transfer Learning Model for Automated Detection of ADHD from fMRI Data

Machine learning for automatic Alzheimer’s disease detection: addressing domain shift issues for building robust models

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