---
library_name: monai
tags:
- classification
- medical
- neuroscience
- alzheimer
license: mit
metrics:
- accuracy
pipeline_tag: image-classification
datasets:
- ADNI
---
---

### Model Description
A machine learning model for waste classification

- **Developed by:** rootstrap
- **Model type:** image-classifier
- **License:** mit

## Waste Classifier Model
The aim is to build a model for MRI classification that identifies among the different classes:

- Alzheimer's
- Mild Cognitive Impairment
- Control

This machine learning model will help medical staff get a second opinion on whether a pacient MRI indicates the presence of Alzheimer's desease 


The model was built using **Monai** MONAI is a freely available, community-supported, PyTorch-based framework for deep learning in healthcare imaging.

It has two main design goals:

To be approachable and rapidly productive
To be also configurable.

### Model Sources 

- **Repository:** [https://github.com/rootstrap/MRI-classifier](https://github.com/rootstrap/MRI-classifier)

## Uses

At Rootstrap we classify waste. We found that people were struggled to classify correctly, 
and then we end up not recycling most of the generated waste at the office, since if there were items in the wrong basket, 
all the basket should not be classified.
Because of this reason, we created an app to help people at our company to classify waste. 

### Direct Use

```bash
model = nets.DenseNet121(spatial_dims=3, in_channels=1, out_channels=3)
checkpoint = torch.load("86_acc_model.pth")
model.load_state_dict(checkpoint)
model.predict()

```

## Bias, Risks, and Limitations

This model has an 86% of accuracy. Although it is an outstanding result, his means that 13% of times the prediction is wrong.
This is why its important to understand that this tool is not a real medical opinion and can not be used as a final diagnosis by any means.
This project does not aim to replace medical staff in diagnosing Alzheimer's desease, instead it is a tool to help them to get a quick and accurate second opinion. 

## Training Details

### Training Data

The MRI data was gathered from the Alzheimer’s Desease Neuroimaging Initiative (ADNI) Database.
Afterwards, the data was splitted into one folder for each class.

Split into train/test 
The data was splitted using the 60% for training, 20% for validation and 20% for testing

The model has been trained to classify waste into 3 classes. 
The dataset used for the training consisted of 2527 images:
- 501 glass
- 594 paper
- 403 cardboard
- 482 plastic
- 410 metal
- 137 trash


### Training Procedure

You can find the code for training at [train.ipynb](https://github.com/rootstrap/MRI-classifier/blob/main/train.ipynb)
Training the model using DenseNet121 adapted for 3D images.

## Evaluation and Results

After 100 epochs, the model reached an accuracy of 85.76%