Performance Analysis of Alzheimer’s Disease Classification Using Large Datasets and Brain Extraction
Implementation Plan :
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Step 1: Initially, We load the ADNI image dataset and also load the test data/image.
Step 2: Next, we perform the Data Preprocessing techniques (e.g., Resize, normalize, normalize) to increase the diversity of your training data.
Step 3: Next, we perform a feature extraction process, In this process we use pre-trained CNN (e.g., VGG16, ResNet).
Step 4: Next, training, Train the model on the training dataset. Then we Evaluate the model’s performance on the test dataset.
Step 5: Next, we consider using transfer learning by fine-tuning a pre-trained model on your dataset.
Step 6: Finally, we evaluate the following performance metrics,like , Precision , Accuracy ,Sensitivity, F1 Score, Specificity.
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Software Requirement:
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1. Tool: Python-3.11.3 or and above version
2. Language: Python
3. Colab Environment
4. OS: Windows 10 – (64-bit)
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Note :-
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1) If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.
