Alzheimer’s Disease Classification: A Hands-On Dataset for Analysts & Data Scientists

Alzheimer’s Disease (AD) is a neurodegenerative disorder that affects millions of people worldwide. Early detection and classification of AD stages can significantly improve patient care and treatment planning. As part of the 20 Days Data Project Challenge, we are introducing a dataset that allows data analysts and data scientists to explore Alzheimer’s classification using machine learning and deep learning techniques.

In this blog post, we will cover:

• How to download the dataset
• The attributes of the dataset
• Exploratory data analysis (EDA) ideas for data analysts
• Power BI analysis for data analysts
• Machine learning models for data scientists

Download the Dataset

The dataset we’ll be working with is available on Kaggle: 👉 Alzheimer’s Multi-Class Dataset

To download it, simply:

1. Sign in to Kaggle (or create an account if you don’t have one).
2. Navigate to the dataset link above.
3. Click the “Download” button.
4. Extract the files and start exploring!

Dataset Overview

The dataset consists of MRI scan images categorized into four different classes:

• Non-Demented: No signs of Alzheimer’s.
• Very Mild Demented: Early-stage signs of Alzheimer’s.
• Mild Demented: More pronounced symptoms.
• Moderate Demented: Advanced stage.

Dataset Attributes

Each image in the dataset is a brain scan categorized into one of the four classes. The dataset has been pre-processed with data augmentation to ensure balance among the classes. The attributes include:

• Image Files: MRI scan images (grayscale or RGB depending on processing needs).
• Labels: Each image is labeled with one of the four Alzheimer’s stages.
• Metadata: Includes augmentation details and class distribution.

Exploratory Data Analysis (EDA) for Data Analysts

Before diving into machine learning, data analysts can perform EDA to understand the dataset better. Some possible analyses include:

1. Class Distribution – Check if the dataset is balanced across different stages of Alzheimer’s.
2. Image Quality Assessment – Visualize samples to ensure clarity and consistency.
3. Histogram Analysis – If metadata includes pixel intensity, analyze brightness variations.
4. Feature Engineering – Extract statistical properties like texture, contrast, or pixel intensity histograms.
5. Data Augmentation Analysis – Compare original vs. augmented data to check for artificial biases.

Power BI Analysis for Data Analysts

For those who prefer Power BI, here are some insights you can generate:

1. Class Distribution Dashboard – Create a bar chart showing the number of images in each class.
2. Heatmap Analysis – Use conditional formatting to analyze pixel intensity trends.
3. Time-Series Analysis – If the dataset contains timestamps, visualize trends over time.
4. Comparative Analysis – Compare augmented vs. original images to measure data balance.
5. Interactive Filtering – Enable drill-down filters to analyze specific subsets of the dataset dynamically.

Machine Learning Approaches for Data Scientists

For data scientists, this dataset provides an excellent opportunity to apply image classification techniques using deep learning. Some recommended models include:

1. Convolutional Neural Networks (CNNs)

• ResNet50: A pre-trained deep learning model known for high accuracy in image classification.
• VGG16/VGG19: Good baseline models for image feature extraction.
• EfficientNet: Optimized for performance and efficiency.

2. Transfer Learning

• Utilize pre-trained models like InceptionV3 or MobileNetV2 and fine-tune on this dataset.
• This approach helps achieve high accuracy with limited training data.

3. Traditional Machine Learning

• Extract Histogram of Oriented Gradients (HOG) or Local Binary Patterns (LBP) as features.
• Train Random Forest, SVM, or XGBoost classifiers on the extracted features.

Steps to Build a Deep Learning Model

1. Load the dataset: Use TensorFlow/Keras or PyTorch for loading and preprocessing.
2. Data Augmentation: Apply techniques like rotation, flipping, and contrast adjustment.
3. Model Selection: Choose a CNN model and load pre-trained weights if using transfer learning.
4. Training & Evaluation: Train the model and validate its performance using accuracy, precision, and recall.
5. Hyperparameter Tuning: Optimize the learning rate, batch size, and dropout layers.

Conclusion

This dataset provides a fantastic opportunity to work on a real-world problem in healthcare AI. Whether you’re a data analyst looking to explore patterns in MRI scans or a data scientist eager to train deep learning models, there’s plenty to learn!