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Alzheimer’s Disease Classification Using MRI Images Project Overview

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.

The model classifies MRI images into four categories:

Non-Demented

Very Mild Demented

Mild Demented

Moderate Demented

Dataset Description

The dataset consists of MRI brain scan images organized into class-specific folders.

Classes & Image Count:

NonDemented: 12,800

VeryMildDemented: 11,200

MildDemented: 10,000

ModerateDemented: 10,000

Although the dataset is not perfectly balanced, class imbalance was handled during training using appropriate techniques (e.g., class weighting).

Source: Kaggle – Alzheimer’s Multiclass Dataset (Equal & Augmented)

Exploratory Data Analysis (EDA)

Basic EDA was performed to:

Inspect class distribution

Verify image quality and consistency

Confirm folder structure and labels

Model & Approach

Model Type: Convolutional Neural Network (CNN)

Technique: Transfer Learning

Base Model: Pretrained CNN (fine-tuned)

Loss Function: Categorical Crossentropy

Evaluation Metrics:

Accuracy

Precision

Recall

Model Performance
The model was trained using transfer learning with fine-tuning and evaluated using multiple performance metrics.

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.

Tools & Technologies

Python

TensorFlow / Keras

NumPy

Matplotlib

Google Colab

KaggleHub

Features

Multi-class MRI image classification

Handles class imbalance during training

Model saving and fine-tuning

Function for testing single images with visualization

📁 Project Structure ├── data/ │ ├── NonDemented/ │ ├── VeryMildDemented/ │ ├── MildDemented/ │ └── ModerateDemented/ ├── notebooks/ │ └── Alzheimer_Classification.ipynb ├── saved_model/ ├── README.md

Author

ADIJAT Motunrayo Oyetoke AI / Machine Learning Enthusiast

Future Improvements

Improve recall for minority classes


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