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A Novel Approach for Alzheimer's Classification Utilizing Ensemble Learning on Pre-trained Neural Networks Fine-tuned on Pre-processed and Augmented Alzheimer's Dataset

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Densenet fine-tuning.ipynb
Densenet fine-tuning.ipynb
 
 
Efficientnet finetuning.ipynb
Efficientnet finetuning.ipynb
 
 
Ensemble fine-tuned pre-trained models.ipynb
Ensemble fine-tuned pre-trained models.ipynb
 
 
Fine-tuning After Augmentation.ipynb
Fine-tuning After Augmentation.ipynb
 
 
Inceptionv3 Unfreeze Last Layers.ipynb
Inceptionv3 Unfreeze Last Layers.ipynb
 
 
Inceptionv3 fine-tuning.ipynb
Inceptionv3 fine-tuning.ipynb
 
 
Pre-process - Intensity Normalization and Slicing.ipynb
Pre-process - Intensity Normalization and Slicing.ipynb
 
 
Pre-process - Intensity Normazation Slicing - Ver2.ipynb
Pre-process - Intensity Normazation Slicing - Ver2.ipynb
 
 
Pre-process - copy_images.ipynb
Pre-process - copy_images.ipynb
 
 
Pre-process Script - Masking.sh
Pre-process Script - Masking.sh
 
 
Pre-process Script - Registration.sh
Pre-process Script - Registration.sh
 
 
README.md
README.md
 
 
Resnet50 Unfreeze Last Layers.ipynb
Resnet50 Unfreeze Last Layers.ipynb
 
 
Resnet50 fine-tuning.ipynb
Resnet50 fine-tuning.ipynb
 
 
Xception fine-tuning.ipynb
Xception fine-tuning.ipynb
 
 

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This study applies a novel approach for Alzheimer's Disease diagnosis utilizing ensemble learning on pre-trained neural networks fine-tuned on Alzheimer's MRI dataset.

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A Novel Approach for Alzheimer's Classification Utilizing Ensemble Learning on Pre-trained Neural Networks Fine-tuned on Pre-processed and Augmented Alzheimer's Dataset

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deep-learning mri ensemble-learning transfer-learning inceptionv3 fine-tune diagnosis data-augmentation alzheimers-disease resnet-50 fine-tuning adni xception densenet121 pre-trained-model efficientnet stacking-ensemble boosting-ensemble

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