Alzheimer's Disease Classification Model Based on MED-3D Transfer Learning

Li, Yanmei; Ding, Weiwu; Wang, XingYu; Li, Lihong; Tang, JingHong

doi:10.1145/3500931.3500999

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…Chen et al [23] proposed the Med3D network pretraining by the datasets from multiple medical competitions (collectively called 3DSeg-8 dataset) in 2019, and the model can be used to segment different organs (brain, lung, heart, blood vessels, prostate, and spleen). Med3D model has subsequently been widely used in disease classification [24] , tumor segmentation [25] , and lung disease classification [26,27] . It can be seen that the encoder of the Med3D model can assist in extracting the characteristic information of medical images and be used to assist in completing clinical medical tasks.…”

Section: Introductionmentioning

confidence: 99%

Ischemic stroke outcome prediction based on the whole-brain features extracted from minimum intensity projection of DSC-PWI images by pre-trained Med3D network

Guo,

Yang,

Zeng

et al. 2023

Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023)

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Accurate stroke outcome prediction is of great significance to making treatment plans and evaluating the rehabilitation state of patients. Previous works paid more attention to the basic information and volume of ischemic tissue for predicting outcomes, ignoring the role of the whole-brain. The purpose of this paper was to prove the value of wholebrain features in outcome prediction. In detail, the pre-trained Med3D model was used to extract whole-brain features from minimum intensity projection (MinIP) of PWI-DSC images, the Least absolute shrinkage and selection operator was used to select outstanding whole-brain features, and ten machine learning models were applied to validate the role of the selected outstanding whole-brain features on predicting outcomes. As the results, when taking ResNet10, ResNet18, ResNet34, and ResNet50 as encoders in the Med3D model, the best AUC of outstanding whole-brain features were 0.88, 0.939, 0.781, and 0.883, and the mean ± std on the ten machine models were 0.756 ± 0.097, 0.766 ± 0.123, 0.714 ± 0.044, and 0.761 ± 0.105, respectively. It can be concluded that the outstanding whole-brain features extracted from the MinIP image can predict good outcomes and poor outcomes for ischemic stroke patients, and the whole-brain features from ResNet18 performed best. The method provided in this study may provide new insight for ischemic stroke research.

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Section: Introductionmentioning

confidence: 99%

Ischemic stroke outcome prediction based on the whole-brain features extracted from minimum intensity projection of DSC-PWI images by pre-trained Med3D network

Guo,

Yang,

Zeng

et al. 2023

Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023)

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“…You can also see our recent systematic review on the current status of using DL in the early diagnosis of AD for a more comprehensive overview (Fathi, Ahmadi, & Dehnad, 2022). Li et al (A. Li et al, 2021) aimed to diagnose AD through a hippocampal shape and asymmetry analysis by cascaded convolutional neural networks (CNN). Compared to their previous study (Cui & Liu, 2019), which used only hippocampal shape features for classi cation, their performance was slightly lower this time.…”

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confidence: 99%

A deep learning-based ensemble method for early diagnosis of Alzheimer's disease using MRI images

Fathi

Ahmadi

Dehnad

et al. 2023

Preprint

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Background Recently, the early diagnosis of Alzheimer's disease has gained major attention due to the growing prevalence of the disease and the resulting costs to individuals and society. The main objective of this study was to propose an ensemble method based on deep learning for the early diagnosis of AD using MRI images.Method The methodology of this study was comprised of collecting the dataset, preprocessing, creating the individual and ensemble models, evaluating the models based on ADNI data, and validating the trained model based on the local dataset. The proposed method was an ensemble approach selected through a comparative analysis of various ensemble scenarios. Finally, the six best individual CNN-based classifiers were selected to combine and constitute the ensemble model.Results The evaluation showed an accuracy rate of 98.57, 96.37, 94.22, 99.83, 93.88, and 93.92, respectively, for NC/AD, NC/EMCI, EMCI/LMCI, LMCI/AD, four-way and three-way classification groups. The validation results on the local dataset revealed an accuracy of 88.46 for three-way classification.Discussion Our performance results were higher than most reviewed studies and comparable with others. Although comparative analysis showed superior results of ensemble methods against individual architectures, there were no significant differences among various ensemble approaches. The validation results revealed that individual models showed low performance in practice. In contrast, the ensemble method showed promising results. However, further studies on various and larger datasets are required to validate the generalizability of the model.

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A Deep Learning-Based Ensemble Method for Early Diagnosis of Alzheimer’s Disease using MRI Images

Fathi,

Ahmadi,

Dehnad

et al. 2023

Neuroinform

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Recently, the early diagnosis of Alzheimer’s disease has gained major attention due to the growing prevalence of the disease and the resulting costs imposed on individuals and society. The main objective of this study was to propose an ensemble method based on deep learning for the early diagnosis of AD using MRI images. The methodology of this study consisted of collecting the dataset, preprocessing, creating the individual and ensemble models, evaluating the models based on ADNI data, and validating the trained model based on the local dataset. The proposed method was an ensemble approach selected through a comparative analysis of various ensemble scenarios. Finally, the six best individual CNN-based classifiers were selected to combine and constitute the ensemble model. The evaluation showed an accuracy rate of 98.57, 96.37, 94.22, 99.83, 93.88, and 93.92 for NC/AD, NC/EMCI, EMCI/LMCI, LMCI/AD, four-way and three-way classification groups, respectively. The validation results on the local dataset revealed an accuracy of 88.46 for three-way classification. Our performance results were higher than most reviewed studies and comparable with others. Although comparative analysis showed superior results of ensemble methods against individual architectures, there were no significant differences among various ensemble approaches. The validation results revealed the low performance of individual models in practice. In contrast, the ensemble method showed promising results. However, further studies on various and larger datasets are required to validate the generalizability of the model.

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Alzheimer's Disease Classification Model Based on MED-3D Transfer Learning

Cited by 5 publications

References 6 publications

Ischemic stroke outcome prediction based on the whole-brain features extracted from minimum intensity projection of DSC-PWI images by pre-trained Med3D network

Ischemic stroke outcome prediction based on the whole-brain features extracted from minimum intensity projection of DSC-PWI images by pre-trained Med3D network

A deep learning-based ensemble method for early diagnosis of Alzheimer's disease using MRI images

A Deep Learning-Based Ensemble Method for Early Diagnosis of Alzheimer’s Disease using MRI Images

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