https://github.com/aishwaryahastak/alzheimer-s-detection

Alzheimer's Detection from Brain MRI scans using DNN
https://github.com/aishwaryahastak/alzheimer-s-detection

deep-neural-networks healthcare image-classification

Last synced: 7 days ago
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Alzheimer's Detection from Brain MRI scans using DNN

• Host: GitHub
• URL: https://github.com/aishwaryahastak/alzheimer-s-detection
• Owner: AishwaryaHastak
• Created: 2024-02-01T17:54:50.000Z (11 months ago)
• Default Branch: main
• Last Pushed: 2024-08-04T17:05:09.000Z (5 months ago)
• Last Synced: 2024-11-08T23:51:53.341Z (2 months ago)
• Topics: deep-neural-networks, healthcare, image-classification
• Language: Jupyter Notebook
• Homepage:
• Size: 626 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# NeuroDetect: MRI‑Based Alzheimer’s Disease Detection using DNN

## 🧠 Overview

Training a Deep Neural Network (DNN) to classify brain MRI scans for detecting Dementia versus Non-Dementia patients. This project aims to use a simpler architecture with fewer parameters while still achieving good accuracy. We focus on a data-centric approach, emphasizing data augmentation and fine-tuning hyperparameters rather than complex model architectures.

![image](https://github.com/user-attachments/assets/79649a4c-ec9b-4614-9f82-b69b31eaf6a9)

The dataset is sourced from Kaggle and can be found at [Alzheimer's Dataset](https://www.kaggle.com/datasets/tourist55/alzheimers-dataset-4-class-of-images)

## 🎯 Objective

The main objective of this project was not to create a model with the highest state-of-the-art (SOTA) accuracy but to understand how data affects model performance. By defining a simple CNN model and keeping it fixed, we focused on using data augmentation techniques and hyperparameter tuning to improve accuracy.

## 💡 Skills

- PyTorch

- Python

- MLFlow

- Data Augmentation

- Hyperparameter Tuning

## ⚖️ Dealing with imbalanced classes

The dataset contains images for 4 classes:

1. Mild Demented

2. Moderate Demented

3. Non Demented

4. Very Mild Demented

   

The class distribution is as follows:



### Techniques to handle class imbalance

The dataset exhibits significant class imbalance. To address this, I employed and compared two data augmentation techniques:

1. **SMOTE** (Synthetic Minority Over-sampling Technique): Equalized the instance counts across all classes.

2. **Manual Augmentation**: Boosted the number of instances in underrepresented classes while retaining the overall class distribution. This approach maintained the relative differences in instance counts, offering a better representation of real-world conditions

**Result**: The model performs better when the class distribution closely mirrors real-life scenarios, providing a more accurate reflection of actual conditions.



## 🛠️Model 

Several models developed on this dataset leveraged pre-trained architectures such as RESNET [1], VGG, and others, achieving remarkable accuracies nearing 94%. Conversely, a different approach employed a more straightforward Convolutional Neural Network model, yielding a validation set accuracy of approximately 80% [2]. This project endeavors to craft a simplified DNN model, boasting fewer parameters than expansive architectures like RESNET, all while upholding high accuracy standards.

## References

[1] [Alzheimer's Detection using DL](https://www.kaggle.com/code/mihirbhatkar/alzheimer-s-detection-using-dl)

[2] [Class Prediction with PyTorch](https://www.kaggle.com/code/natsu18/class-prediction-pytorch)