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| Alzheimer’s Disease Classification Using MRI Images Project Overview |
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| This project focuses on classifying Alzheimer’s disease stages from brain MRI scan images using deep learning. The goal is to build a multi-class image classification model that can distinguish between different stages of Alzheimer’s disease to support early diagnosis and research. |
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| The model classifies MRI images into four categories: |
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| Non-Demented |
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| Very Mild Demented |
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| Mild Demented |
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| Moderate Demented |
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| Dataset Description |
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| The dataset consists of MRI brain scan images organized into class-specific folders. |
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| Classes & Image Count: |
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| NonDemented: 12,800 |
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| VeryMildDemented: 11,200 |
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| MildDemented: 10,000 |
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| ModerateDemented: 10,000 |
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| Although the dataset is not perfectly balanced, class imbalance was handled during training using appropriate techniques (e.g., class weighting). |
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| Source: Kaggle – Alzheimer’s Multiclass Dataset (Equal & Augmented) |
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| Exploratory Data Analysis (EDA) |
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| Basic EDA was performed to: |
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| Inspect class distribution |
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| Verify image quality and consistency |
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| Confirm folder structure and labels |
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| Model & Approach |
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| Model Type: Convolutional Neural Network (CNN) |
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| Technique: Transfer Learning |
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| Base Model: Pretrained CNN (fine-tuned) |
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| Loss Function: Categorical Crossentropy |
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| Evaluation Metrics: |
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| Accuracy |
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| Precision |
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| Recall |
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| Model Performance |
| The model was trained using transfer learning with fine-tuning and evaluated using multiple performance metrics. |
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| Best Validation Results |
| Validation Accuracy: 96.23% |
| Validation AUC: ~0.995 |
| Validation Precision: ~96.1% |
| Validation Recall: ~95.8% |
| Training Insights |
| The model achieved ~99% training accuracy while maintaining strong generalization. |
| Early stopping was applied to prevent overfitting. |
| Model weights were restored from the epoch with the highest validation accuracy. |
| These results indicate that the model effectively distinguishes between the four Alzheimer’s disease stages with high reliability. |
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| Tools & Technologies |
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| Python |
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| TensorFlow / Keras |
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| NumPy |
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| Matplotlib |
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| Google Colab |
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| KaggleHub |
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| Features |
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| Multi-class MRI image classification |
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| Handles class imbalance during training |
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| Model saving and fine-tuning |
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| Function for testing single images with visualization |
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| 📁 Project Structure ├── data/ │ ├── NonDemented/ │ ├── VeryMildDemented/ │ ├── MildDemented/ │ └── ModerateDemented/ ├── notebooks/ │ └── Alzheimer_Classification.ipynb ├── saved_model/ ├── README.md |
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| Author |
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| ADIJAT Motunrayo Oyetoke AI / Machine Learning Enthusiast |
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| Future Improvements |
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| Improve recall for minority classes |
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| ``` |
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